bge-small-en-v1.5
Text Embeddings • BAAI
@cf/baai/bge-small-en-v1.5
- Cloudflare-hosted
- Batch
BAAI general embedding (Small) model that transforms any given text into a 384-dimensional vector
| Model Info | |
|---|---|
| More information | link ↗ |
| Maximum Input Tokens | 512 |
| Output Dimensions | 384 |
| Batch | Yes |
| Unit Pricing | $0.0202 per M input tokens |
Usage
export interface Env {
AI: Ai;
}
export default {
async fetch(request, env): Promise<Response> {
// Can be a string or array of strings]
const stories = [
"This is a story about an orange cloud",
"This is a story about a llama",
"This is a story about a hugging emoji",
];
const embeddings = await env.AI.run(
"@cf/baai/bge-small-en-v1.5",
{
text: stories,
}
);
return Response.json(embeddings);
},
} satisfies ExportedHandler<Env>;
import os
import requests
ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")
stories = [
'This is a story about an orange cloud',
'This is a story about a llama',
'This is a story about a hugging emoji'
]
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/baai/bge-small-en-v1.5",
headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
json={"text": stories}
)
print(response.json())
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/baai/bge-small-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"] }'
OpenAI compatible endpoints
Workers AI also supports OpenAI compatible API endpoints for /v1/chat/completions and /v1/embeddings. For more details, refer to Configurations.
Parameters
<details>
<summary>Synchronous — Send a request and receive a complete response</summary>
▶text
<code>one of</code>required
pooling
<code>string</code>default: meanenum: mean, clsThe pooling method used in the embedding process. cls pooling will generate more accurate embeddings on larger inputs - however, embeddings created with cls pooling are not compatible with embeddings generated with mean pooling. The default pooling method is mean in order for this to not be a breaking change, but we highly suggest using the new cls pooling for better accuracy.
▶shape[]
<code>array</code>
▶data[]
<code>array</code>Embeddings of the requested text values
pooling
<code>string</code>enum: mean, clsThe pooling method used in the embedding process.
</details>
<details>
<summary>Batch — Send multiple requests in a single API call</summary>
▶requests[]
<code>array</code>requiredBatch of the embeddings requests to run using async-queue
▶shape[]
<code>array</code>
▶data[]
<code>array</code>Embeddings of the requested text values
pooling
<code>string</code>enum: mean, clsThe pooling method used in the embedding process.
</details>
API Schemas (Raw)
SynchronousInput Open Download
SynchronousOutput Open Download
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Documentation Index
Fetch the complete documentation index at: https://developers.cloudflare.com/llms.txt
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