Create Embedding

📘 API Introduction (Embeddings API)

The Embeddings API is used to convert text into high-dimensional vectors (vector representation), which are widely applied in scenarios such as semantic search, similarity calculation, clustering, and recommendation systems.

This endpoint maps text onto a fixed-dimension floating-point array (vector) to serve as a machine-understandable semantic representation.

🎯 Applicable Scenarios
🔎 Semantic Search

Perform similarity matching between user queries and document vectors (e.g., knowledge base retrieval)

🤖 RAG (Retrieval-Augmented Generation)

Combine with vector databases to achieve context-enhanced Q&A

📊 Text Clustering

Automatically classify text based on semantic similarity

🧠 Recommendation Systems

Execute recommendation ranking based on the semantic similarity of content

🔐 Authentication Method

All requests must be authenticated via a Bearer Token.

Header Example
Authorization: Bearer sk-xxxxxxxxxx
Content-Type: application/json

curl --location 'https://api.aboai.ai/v1/embeddings' \ --header 'Authorization: Bearer {{api_key}}' \ --header 'Content-Type: application/json' \ --data '{ "input": "Hello World", "model": "text-embedding-3-large", "encoding_format": "float", "dimensions": 1536, "user": "user-1234" }'

{ "object": "list", "data": [ { "object": "embedding", "index": 0, "embedding": [ 0.0023063174, -0.009361042, 0.015764702, -0.007297516, 0.004337311, -0.00058698654, 0.0006623268, 0.02305603, -0.005214691, -0.01725769, -0.005870819, 0.015945435, -0.015335083, 0.0005760193, 0.025787354, 0.014816284, -0.023544312, -0.015090942, 0.006893158, -0.008895874, -0.016586304, 0.0035057068 ] } ], "model": "text-embedding-3-large", "usage": { "prompt_tokens": 16, "total_tokens": 16 } }

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