from mka1 import SDK
with SDK(
bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:
res = sdk.llm.embeddings.list_models()
# Handle response
print(res){
"object": "list",
"data": [
{
"id": "meetkai:embedding",
"provider": "meetkai",
"model": "embedding",
"limits": {
"max_batch_size": 1000,
"max_input_tokens": 32768,
"max_input_length": 65536
}
}
]
}Returns a list of available embedding models with their limits. Use this endpoint to discover which models are available and their constraints (batch size, input length) before making embedding requests.
from mka1 import SDK
with SDK(
bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:
res = sdk.llm.embeddings.list_models()
# Handle response
print(res){
"object": "list",
"data": [
{
"id": "meetkai:embedding",
"provider": "meetkai",
"model": "embedding",
"limits": {
"max_batch_size": 1000,
"max_input_tokens": 32768,
"max_input_length": 65536
}
}
]
}Gateway auth: send Authorization: Bearer <mka1-api-key>. For multi-user server-side integrations, you can also send X-On-Behalf-Of: <external-user-id>.
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