from openai import OpenAI
import os
# 💥 Create an OpenAI client, pointed at Muna
openai = OpenAI(
api_key=os.environ["MUNA_API_KEY"],
base_url="https://inference.muna.ai/v1"
)
# 🔥 Create a text embedding
embedding = openai.embeddings.create(
model="@nomic/nomic-embed-text-v1.5",
input="What is the capital of France?"
)
# 🚀 Use the embedding
print(embedding.data[0].embedding)
import OpenAI from "openai"
// 💥 Create an OpenAI client, pointed at Muna
const openai = new OpenAI({
apiKey: process.env.MUNA_API_KEY,
baseURL: "https://inference.muna.ai/v1"
});
// 🔥 Create a text embedding
const embedding = await openai.embeddings.create({
model: "@nomic/nomic-embed-text-v1.5",
input: "What is the capital of France?"
});
// 🚀 Use the embedding
console.log(embedding.data[0].embedding);
curl https://inference.muna.ai/v1/embeddings \
-H "Authorization: Bearer $MUNA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@nomic/nomic-embed-text-v1.5",
"input": "What is the capital of France?"
}'
{
"object": "list",
"model": "@nomic/nomic-embed-text-v1.5",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0123, -0.0456, 0.0789]
}
],
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
}
}
OpenAI
Create an Embedding
POST
/
v1
/
embeddings
from openai import OpenAI
import os
# 💥 Create an OpenAI client, pointed at Muna
openai = OpenAI(
api_key=os.environ["MUNA_API_KEY"],
base_url="https://inference.muna.ai/v1"
)
# 🔥 Create a text embedding
embedding = openai.embeddings.create(
model="@nomic/nomic-embed-text-v1.5",
input="What is the capital of France?"
)
# 🚀 Use the embedding
print(embedding.data[0].embedding)
import OpenAI from "openai"
// 💥 Create an OpenAI client, pointed at Muna
const openai = new OpenAI({
apiKey: process.env.MUNA_API_KEY,
baseURL: "https://inference.muna.ai/v1"
});
// 🔥 Create a text embedding
const embedding = await openai.embeddings.create({
model: "@nomic/nomic-embed-text-v1.5",
input: "What is the capital of France?"
});
// 🚀 Use the embedding
console.log(embedding.data[0].embedding);
curl https://inference.muna.ai/v1/embeddings \
-H "Authorization: Bearer $MUNA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@nomic/nomic-embed-text-v1.5",
"input": "What is the capital of France?"
}'
{
"object": "list",
"model": "@nomic/nomic-embed-text-v1.5",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0123, -0.0456, 0.0789]
}
],
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
}
}
Creates text embeddings.
from openai import OpenAI
import os
# 💥 Create an OpenAI client, pointed at Muna
openai = OpenAI(
api_key=os.environ["MUNA_API_KEY"],
base_url="https://inference.muna.ai/v1"
)
# 🔥 Create a text embedding
embedding = openai.embeddings.create(
model="@nomic/nomic-embed-text-v1.5",
input="What is the capital of France?"
)
# 🚀 Use the embedding
print(embedding.data[0].embedding)
import OpenAI from "openai"
// 💥 Create an OpenAI client, pointed at Muna
const openai = new OpenAI({
apiKey: process.env.MUNA_API_KEY,
baseURL: "https://inference.muna.ai/v1"
});
// 🔥 Create a text embedding
const embedding = await openai.embeddings.create({
model: "@nomic/nomic-embed-text-v1.5",
input: "What is the capital of France?"
});
// 🚀 Use the embedding
console.log(embedding.data[0].embedding);
curl https://inference.muna.ai/v1/embeddings \
-H "Authorization: Bearer $MUNA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@nomic/nomic-embed-text-v1.5",
"input": "What is the capital of France?"
}'
{
"object": "list",
"model": "@nomic/nomic-embed-text-v1.5",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0123, -0.0456, 0.0789]
}
],
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
}
}
Body
string
required
Model tag.
string | string[]
required
Input text to embed. Pass a list of texts to embed them in a single request.
integer
Number of embedding dimensions, for models that support Matryoshka representation learning.
string
Embedding encoding format:
float or base64. Defaults to float.Response
string
required
Object type, always
list.string
required
Model tag.
Embedding[]
required