Router.Embeddings
Create an Embedding
Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.from orq_ai_sdk import Orq
import os
with Orq(
api_key=os.getenv("ORQ_API_KEY", ""),
) as orq:
res = orq.router.embeddings.create(input=[
"The food was delicious",
"And the waiter was friendly",
], model="openai/text-embedding-3-small", orq={
"identity": {
"id": "contact_01ARZ3NDEKTSV4RRFFQ69G5FAV",
"display_name": "Jane Doe",
"email": "jane.doe@example.com",
"metadata": [
{
"department": "Engineering",
"role": "Senior Developer",
},
],
"logo_url": "https://example.com/avatars/jane-doe.jpg",
"tags": [
"hr",
"engineering",
],
},
})
# Handle response
print(res)
Show Parameters
Show Parameters
{
"input": Union[str, List[Input2]], # required
"model": str, # required
"cache": { # optional
"ttl": Optional[int],
"type": Literal["exact_match"], # required
},
"dimensions": Optional[int],
"encoding_format": Optional[Literal["float", "base64"]],
"fallbacks": { # optional
"model": str, # required
},
"load_balancer": { # optional
"models": { # required
"model": str, # required
"weight": float, # required
},
"type": Literal["weight_based"], # required
},
"name": Optional[str],
"orq": { # optional
"cache": Optional[EmbeddingCacheConfig],
"contact": { # optional
"display_name": Optional[str],
"email": Optional[str],
"id": str, # required
"metadata": List[Dict[str, Any]],
"tags": List[str],
},
"fallbacks": List[FallbackConfig],
"identity": { # optional
"id": str, # required
"display_name": Optional[str],
"email": Optional[str],
"metadata": List[Dict[str, Any]],
"logo_url": Optional[str],
"tags": List[str],
},
"load_balancer": Optional[EmbeddingLoadBalancerConfig],
"name": Optional[str],
"retry": { # optional
"count": int, # required
"on_codes": List[int], # required
},
"timeout": { # optional
"call_timeout": int, # required
},
},
"retry": { # optional
"count": int, # required
"on_codes": List[int], # required
},
"timeout": { # optional
"call_timeout": int, # required
},
"user": Optional[str],
}
Show Response
Show Response
{
"data": {
"embedding": Any,
"index": int,
"object": Literal["embedding"],
},
"model": str,
"object": Literal["list"],
"usage": {
"prompt_tokens": int,
"total_tokens": int,
},
}