Embeddings API

An embedding is a vector representation of a piece of data. Text embeddings are often used to capture the semantic meaning of a piece of text, where the distance between text embedding vectors is used to measure their relatedness.

Create Embeddings

Endpoint to create an embedding vector representation of a text input.

POST https://api.relax.ai/v1/embeddings

Example Request

relaxAI SDKs

pythonnode.jsgocurl

from relaxai import Relaxai

client = Relaxai(api_key=RELAX_API_KEY)

response = client.embeddings.create_embedding(
  model="Mistral-7b-embedding",
  input="The capital city of the UK is London",
  encoding_format="float"
)

print(response)

import { Relaxai } from 'relaxai';

const relaxai = new Relaxai({
apiKey: RELAX_API_KEY
});

const embeddingResponse = await client.embeddings.createEmbedding({
input: "The capital city of the UK is London",
model: "Mistral-7b-embedding",
});

console.log(embeddingResponse.data);

package main

import (
"context"
"fmt"

"github.com/relax-ai/go-sdk"
"github.com/relax-ai/go-sdk/option"
)

func main() {
client := relaxai.NewClient(
  option.WithAPIKey("RELAX_API_KEY"),
)
embeddingResponse, err := client.Embeddings.NewEmbedding(context.TODO(), relaxai.EmbeddingNewEmbeddingParams{
  EmbeddingRequest: relaxai.EmbeddingRequestParam{
    Input: map[string]interface{}{
    },
    Model: "Mistral-7b-embedding",
  },
})
if err != nil {
  panic(err.Error())
}
fmt.Printf("%+v
", embeddingResponse.Data)
}

curl https://api.relax.ai/v1/embeddings \
-H "Authorization: Bearer $RELAX_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "input": "The capital city of the UK is London",
  "model": "Mistral-7b-embedding",
  "encoding_format": "float"
}'

OpenAI SDKs

pythonnode.jscurl

from openai import OpenAI

client = OpenAI(
api_key=RELAX_API_KEY,
base_url='https://api.relax.ai/v1/'
)

response = client.embeddings.create(
model="Mistral-7b-embedding",
input="The capital city of the UK is London",
encoding_format="float"
)

print(response)

import OpenAI from "openai";

const openai = new OpenAI({
apiKey: RELAX_API_KEY,
baseURL: 'https://api.relax.ai/v1/'
});

async function main() {
const embedding = await openai.embeddings.create({
  model: "Mistral-7b-embedding",
  input: "The capital city of the UK is London",
  encoding_format: "float",
});

console.log(embedding);
}

main();

curl https://api.relax.ai/v1/embeddings \
-H "Authorization: Bearer $RELAX_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "input": "The capital city of the UK is London",
  "model": "Mistral-7b-embedding",
  "encoding_format": "float"
}'

---

Response

Returns an embedding object, which contains the embedding vector of the text input.

Embedding Response

{
 "object": "list",
 "data": [
  {
   "object": "embedding",
   "embedding": [
    -0.002363205,
    0.005371202,
    ...
   ],
   "index": 0
  }
 ],
 "model": "Mistral-7b-embedding",
 "usage": {
  "prompt_tokens": 10,
  "completion_tokens": 0,
  "total_tokens": 10,
  "prompt_tokens_details": null,
  "completion_tokens_details": null
 }
}

---

Request Body

The following parameters can be included in the request body:

---

Create Embeddings Request Body

model

• Type: string
• Required: Yes
• Description: The model name to use for generating the completion.

---

input

• Type: string or array
• Required: Yes
• Description: Input text to embed. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max token length of the model (4096 tokens for the Mistral-7b-embedding model) and cannot be an empty string.

---

encoding_format

• Type: string
• Required: No
• Description: Embeddings vector format. Either float or base64.

---