bge-base-en-v1.5
Model ID: @cf/baai/bge-base-en-v1.5
BAAI general embedding (bge) models transform any given text into a compact vector
 Properties
Task Type: Text Embeddings
Max input tokens: 512
Output dimensions: 768
 Code Examples
Worker - 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/baai/bge-base-en-v1.5",      {        text: stories,      }    );
    return Response.json(embeddings);  },
} satisfies ExportedHandler<Env>;
Python
import osimport 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/baai/bge-base-en-v1.5",  headers={"Authorization": "Bearer {AUTH_TOKEN}"},  json={"text": stories}
)
print(response.json())
curl
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/baai/bge-base-en-v1.5 \  -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"] }'
 Responses
Single string:
{  "shape":[1,768],  "data": [    [0.03190500661730766, 0.006071353796869516, 0.025971125811338425,...]  ]
}
Batch of two strings:
{  "shape":[2,768],  "data":[    [0.03190416097640991, 0.006062490865588188, 0.025968171656131744,...],    [0.002439928939566016, -0.021352028474211693, 0.06229676678776741,...],    [-0.02154572866857052,0.09098546206951141,0.006273532286286354,...]  ]
}
 API Schema
The following schema is based on JSON SchemaInput JSON Schema
Output JSON Schema