Introduction Embeddings

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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> with nomic_v1_5)

All our models are optimized for inference.

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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

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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.