b
bge-base-en-v1.5
Text Embeddings • baaiBAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector
Features | |
---|---|
More information | link ↗ |
Maximum Input Tokens | 512 |
Output Dimensions | 768 |
Beta | Yes |
Usage
Workers - 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": f"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"] }'
Parameters
* indicates a required field
Input
-
text *
one of-
0
string min 1The text to embed
-
1
arrayBatch of text values to embed
-
items
string min 1The text to embed
-
-
Output
-
shape
array-
items
number
-
-
data
arrayEmbeddings of the requested text values
-
items
arrayFloating point embedding representation shaped by the embedding model
-
items
number
-
-
API Schemas
The following schemas are based on JSON Schema
{ "type": "object", "properties": { "text": { "oneOf": [ { "type": "string", "description": "The text to embed", "minLength": 1 }, { "type": "array", "description": "Batch of text values to embed", "items": { "type": "string", "description": "The text to embed", "minLength": 1 }, "maxItems": 100 } ] } }, "required": [ "text" ]}
{ "type": "object", "contentType": "application/json", "properties": { "shape": { "type": "array", "items": { "type": "number" } }, "data": { "type": "array", "description": "Embeddings of the requested text values", "items": { "type": "array", "description": "Floating point embedding representation shaped by the embedding model", "items": { "type": "number" } } } }}
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