Introduction Embeddings
Example section for showcasing API endpoints
Quick Example
curl -X POST https://fastapi.mymagic.ai/v1/embeddings \
-H 'Authorization: Bearer <your personal access token>' \
-H 'Content-Type: application/json' \
-d '{
"model": "<model>",
"question": "<your question>",
"storage_provider": "<your storage provider>",
"bucket_name": "<your bucket name>",
"session": "<your session name>",
"max_tokens": "<number of tokens to output>",
"system_prompt": "<your system prompt>",
"role_arn": "arn:aws:iam::<your aws account ID>:role/<your s3 access role>",
"region": "The region your bucket is in",
"return_output": "Boolean indicating whether to return the output or not",
"input_json_file": "The name of the input json file in your s3 bucket",
"structured_output": "json schema for the response output"
}'
Currently the API supports the following llm:
- Nomic-embed-text-v1.5 (replace
<model>
withnomic_v1_5
)
All our models are optimized for inference.
AWS S3
Please use the s3 access role you created in the previous step. Also, put your files for batch inference in a folder called <personal_access_token>/<session_name>
in your s3 bucket. If you name your session my_session
, then you should put your files in the following folder: <personal_access_token>/my_session
GCS
For using GCS, you will need to set up a service account with the necessary permissions to access bucket. Place your files for batch inference in a bucket, ideally under a folder named <personal_access_token>/<session_name>
. If you name your session my_session
, then you should put your files in the following folder: <personal_access_token>/my_session
.