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Copy pathembedding_model.py
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66 lines (52 loc) · 2.16 KB
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from graxon.embedding_models.types import EmbeddingModelCreateParams, EmbeddingModelProvider
from graxon.client import GraxonAsyncClient
import asyncio
async def main():
base_url = "http://localhost:8888"
api_key = "graxon_api_key"
timeout = 60
client = GraxonAsyncClient(api_key=api_key, base_url=base_url, timeout=timeout)
org_id = "test"
# create embedding model
create_response = await client.embedding_models.create(org_id, request=EmbeddingModelCreateParams(
org_id=org_id,
name="OpenAI Test Model",
model_name="text-embedding-ada-002",
model_id="text-embedding-ada-002",
provider=EmbeddingModelProvider.OPENAI,
dimension=1536,
description="OpenAI Test Model"
))
print("\ncreate_response", create_response)
# Create Multiple Embedding Models
multiple_create_response = await client.embedding_models.create_multiple(org_id, [
EmbeddingModelCreateParams(
org_id=org_id,
name="GEMINI Test Model",
model_name="text-embedding-001 Model",
model_id="text-embedding-001",
provider=EmbeddingModelProvider.GEMINI,
dimension=1536,
description="GEMINI Test Model"
),
EmbeddingModelCreateParams(
org_id=org_id,
name="VOYAGE Test Model",
model_name="embedding VOYAGE",
model_id="voyage-4-large",
provider=EmbeddingModelProvider.VOYAGE,
dimension=1536,
description="VOYAGE Test Model"
)
])
print("\nmultiple_create_response", multiple_create_response)
# Get
get_response = await client.embedding_models.get(org_id=org_id, embedding_model_id=create_response.id)
print("\nget_response", get_response)
# List By Provider
list_response = await client.embedding_models.list_by_provider(org_id=org_id, provider=EmbeddingModelProvider.OPENAI)
print("\nlist_response", list_response)
# Delete
delete_response = await client.embedding_models.delete(org_id=org_id, embedding_model_id=create_response.id)
print("\ndelete_response", delete_response)
asyncio.run(main())