Generate embeddings
- Python
- Node.js
- Go
from meshapi import MeshAPI, EmbeddingsParams
client = MeshAPI(base_url="https://api.meshapi.ai", token="rsk_...")
result = client.embeddings.create(
EmbeddingsParams(
model="openai/text-embedding-3-small",
input="MeshAPI embeddings smoke test",
)
)
print(result.model) # model used
print(len(result.data)) # number of embedding items
print(len(result.data[0].embedding)) # vector dimension
const result = await client.embeddings.create({
model: "openai/text-embedding-3-small",
input: "MeshAPI embeddings smoke test",
});
console.log(result.model);
console.log(result.data[0].embedding.length); // vector dimension
model := "openai/text-embedding-3-small"
resp, err := client.Embeddings.Create(ctx, meshapi.EmbeddingsParams{
Model: &model,
Input: "MeshAPI embeddings smoke test",
})
if err != nil {
log.Fatal(err)
}
fmt.Printf("model=%s items=%d dims=%d\n", resp.Model, len(resp.Data), len(resp.Data[0].Embedding.Floats()))
Batch embeddings
Pass a slice/array of strings to embed multiple inputs in a single request.- Python
- Node.js
- Go
result = client.embeddings.create(
EmbeddingsParams(
model="openai/text-embedding-3-small",
input=["First document", "Second document", "Third document"],
)
)
for item in result.data:
print(f"index={item.index} dims={len(item.embedding)}")
const result = await client.embeddings.create({
model: "openai/text-embedding-3-small",
input: ["First document", "Second document", "Third document"],
});
result.data.forEach(item => {
console.log(`index=${item.index} dims=${item.embedding.length}`);
});
model := "openai/text-embedding-3-small"
resp, err := client.Embeddings.Create(ctx, meshapi.EmbeddingsParams{
Model: &model,
Input: []string{"First document", "Second document", "Third document"},
})
for _, item := range resp.Data {
fmt.Printf("index=%d dims=%d\n", item.Index, len(item.Embedding.Floats()))
}
Response fields
| Field | Description |
|---|---|
result.data | Array of embedding objects |
result.data[i].embedding | Float vector |
result.data[i].index | Position in the input array |
result.model | Model used |
result.usage | Token counts |