> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Cloudflare workers AI integration

> Integrate with the Cloudflare workers AI embedding model using LangChain Python.

> [Cloudflare, Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Cloudflare) is an American company that provides content delivery network services, cloud cybersecurity, DDoS mitigation, and ICANN-accredited domain registration services.

> [Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/) allows you to run machine learning models, on the `Cloudflare` network, from your code via REST API.

> [Workers AI Developer Docs](https://developers.cloudflare.com/workers-ai/models/text-embeddings/) lists all text embeddings models available.

## Setting up

Both a Cloudflare Account ID and Workers AI API token are required. Find how to obtain them from [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/).

You can pass these parameters explicitly or define as environmental variables.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import os

from dotenv import load_dotenv

load_dotenv(".env")

cf_acct_id = os.getenv("CF_ACCOUNT_ID")

cf_ai_token = os.getenv("CF_AI_API_TOKEN")
```

## Example

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_cloudflare.embeddings import (
    CloudflareWorkersAIEmbeddings,
)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
embeddings = CloudflareWorkersAIEmbeddings(
    account_id=cf_acct_id,
    api_token=cf_ai_token,
    model_name="@cf/baai/bge-small-en-v1.5",
)
# single string embeddings
query_result = embeddings.embed_query("test")
len(query_result), query_result[:3]
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
(384, [-0.033660888671875, 0.039764404296875, 0.03558349609375])
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# string embeddings in batches
batch_query_result = embeddings.embed_documents(["test1", "test2", "test3"])
len(batch_query_result), len(batch_query_result[0])
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
(3, 384)
```

***

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