--- description: BAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector title: bge-base-en-v1.5 image: https://developers.cloudflare.com/og-docs.png --- [Skip to content](#main-content) > Documentation Index > Fetch the complete documentation index at: https://developers.cloudflare.com/llms.txt > Use this file to discover all available pages before exploring further. ![BAAI logo](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/_astro/baai.BooZR_xF.svg) # bge-base-en-v1.5 Text Embeddings • BAAI Copy as Markdown|[View as Markdown](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/models/bge-base-en-v1.5/index.md)|[Agent setup](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/agent-setup/) `@cf/baai/bge-base-en-v1.5` * Cloudflare-hosted * Batch BAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector | Model Info | | | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------ | | Context Window[ ↗](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/platform/glossary/) | 153,600 tokens | | More information | [link ↗](https://huggingface.co/BAAI/bge-base-en-v1.5) | | Maximum Input Tokens | 512 | | Output Dimensions | 768 | | Batch | Yes | | Unit Pricing | $0.067 per M input tokens | ## Usage ```ts export interface Env { AI: Ai; } export default { async fetch(request, env): Promise { // Can be a string or array of strings] const stories = [ "This is a story about an orange cloud", "This is a story about a llama", "This is a story about a hugging emoji", ]; const embeddings = await env.AI.run( "@cf/baai/bge-base-en-v1.5", { text: stories, } ); return Response.json(embeddings); }, } satisfies ExportedHandler; ``` ```py import os import requests ACCOUNT_ID = "your-account-id" AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN") stories = [ 'This is a story about an orange cloud', 'This is a story about a llama', 'This is a story about a hugging emoji' ] response = requests.post( f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/baai/bge-base-en-v1.5", headers={"Authorization": f"Bearer {AUTH_TOKEN}"}, json={"text": stories} ) print(response.json()) ``` ```sh curl https://api.cloudflare.com/client/v4/accounts/$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"] }' ``` OpenAI compatible endpoints Workers AI also supports OpenAI compatible API endpoints for `/v1/chat/completions` and `/v1/embeddings`. For more details, refer to [Configurations](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/configuration/open-ai-compatibility/). ## Parameters Synchronous — Send a request and receive a complete response ▶text `one of`required pooling `string`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\[\] `array` ▶data\[\] `array`Embeddings of the requested text values pooling `string`enum: mean, clsThe pooling method used in the embedding process. Batch — Send multiple requests in a single API call ▶requests\[\] `array`requiredBatch of the embeddings requests to run using async-queue ▶shape\[\] `array` ▶data\[\] `array`Embeddings of the requested text values pooling `string`enum: mean, clsThe pooling method used in the embedding process. ## API Schemas (Raw) SynchronousInput SynchronousOutput BatchInput BatchOutput Was this helpful? YesNo ## On this page [![](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/_astro/logo.te5VL_aD.svg)Docs](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/) ```json {"@context":"https://schema.org","@type":"TechArticle","@id":"https://developers.cloudflare.com/workers-ai/models/bge-base-en-v1.5/#page","headline":"bge-base-en-v1.5 (BAAI) · Cloudflare AI docs · Cloudflare Workers AI docs","description":"BAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector","url":"https://developers.cloudflare.com/workers-ai/models/bge-base-en-v1.5/","inLanguage":"en","image":"https://developers.cloudflare.com/og-docs.png","publisher":{"@type":"Organization","name":"Cloudflare","description":"One platform for your apps, agents, and workforce. 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