Text Similarity

Compare two text strings for semantic similarity using Workers AI embeddings and cosine similarity. Returns a score between 0 and 1.

Features

• Compare two text strings for semantic similarity (score 0–1)
• Workers AI embeddings via @cf/baai/bge-base-en-v1.5
• Cosine similarity computed at the edge
• Stateless GET API with text1 and text2 query parameters

API Reference

GET /similarity

Computes the cosine similarity between embeddings of two text inputs.

Example Request

curl "https://your-worker.workers.dev/similarity?text1=hello%20world&text2=hello%20there"

Success Response

text1 string

The first input text

text2 string

The second input text

similarity number

Cosine similarity score between 0 and 1 (higher means more similar)

{
  "text1": "hello world",
  "text2": "hello there",
  "similarity": 0.82
}

Error Response

{
  "error": "Missing or invalid query parameter: text1",
  "code": "INVALID_TEXT1"
}

Error Codes

• 400 - Missing or invalid text1 (INVALID_TEXT1)
• 400 - Missing or invalid text2 (INVALID_TEXT2)
• 502 - Workers AI model run failed (AI_ERROR)

Use Cases

• Detect duplicate or near-duplicate content at the edge
• Rank search results or FAQ matches by semantic relevance
• Learn Workers AI embedding models and vector similarity
• Build lightweight deduplication or matching APIs without a vector database

Limitations

• Workers AI is subject to usage limits by plan
• Each input string length is capped
• Compares two strings only; no corpus or vector database search

Deployment

curl "https://your-worker.workers.dev/similarity?text1=hello&text2=hi"

Local Development

cd apps/experiments/text-similarity
npm install
npm run dev

Test locally:

curl "http://localhost:8787/similarity?text1=hello&text2=hi"

Cloudflare Features Used

• Workers - Edge compute runtime
• Workers AI - @cf/baai/bge-base-en-v1.5 text embedding model