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plamo-embedding-1b

Text Embeddings • pfnet
@cf/pfnet/plamo-embedding-1b

PLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, Inc. It can convert Japanese text input into numerical vectors and can be used for a wide range of applications, including information retrieval, text classification, and clustering.

Model Info
Unit Pricing$0.019 per M input tokens

Usage

Workers - TypeScript
TypeScript
export interface Env {
  AI: Ai;
}


export default {
  async fetch(request, env): Promise<Response> {


    // Can be a string or array of strings]
    const stories = [
      "This is a story about an orange cloud",
      "This is a story about a llama",
      "This is a story about a hugging emoji",
    ];


    const embeddings = await env.AI.run(
      "@cf/pfnet/plamo-embedding-1b",
      {
        text: stories,
      }
    );


    return Response.json(embeddings);
  },
} satisfies ExportedHandler<Env>;

Python
Python
import os
import requests




ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")


stories = [
  'This is a story about an orange cloud',
  'This is a story about a llama',
  'This is a story about a hugging emoji'
]


response = requests.post(
  f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/pfnet/plamo-embedding-1b",
  headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
  json={"text": stories}
)


print(response.json())

curl
Terminal window
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/pfnet/plamo-embedding-1b  \
  -X POST  \
  -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN"  \
  -d '{ "text": ["This is a story about an orange cloud", "This is a story about a llama", "This is a story about a hugging emoji"] }'

Parameters

* indicates a required field

Input

  • text one of required

    • 0 string

      Input text to embed. Can be a single string or a list of strings.

    • 1 array

      Input text to embed. Can be a single string or a list of strings.

      • items string

Output

  • data array required

    Embedding vectors, where each vector is a list of floats.

    • items array

      • items number

  • shape array required

    Shape of the embedding data as [number_of_embeddings, embedding_dimension].

    • items integer

API Schemas

The following schemas are based on JSON Schema

{
    "type": "object",
    "properties": {
        "text": {
            "oneOf": [
                {
                    "type": "string"
                },
                {
                    "type": "array",
                    "items": {
                        "type": "string"
                    }
                }
            ],
            "description": "Input text to embed. Can be a single string or a list of strings."
        }
    },
    "required": [
        "text"
    ]
}