Modèles de texte
Modèles d'image
Modèles vidéo
Modération du contenu
Embeddings
POST /v1/embeddings génère des embeddings de texte avec un modèle choisi pour les flux de travail de recherche sémantique, de clustering et de récupération via CometAPI.
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key="<COMETAPI_KEY>",
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="The food was delicious and the waiter was friendly.",
)
print(response.data[0].embedding[:5]) # First 5 dimensions
print(f"Dimensions: {len(response.data[0].embedding)}"){
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [
-0.0021,
-0.0491,
0.0209,
0.0314,
-0.0453
]
}
],
"model": "text-embedding-3-small",
"usage": {
"prompt_tokens": 2,
"total_tokens": 2
}
}CometAPI prend en charge les modèles d’embedding de plusieurs fournisseurs via un endpoint unique. Transmettez une ou plusieurs chaînes de texte et recevez des vecteurs numériques pour la recherche sémantique, le clustering, la classification ou la génération augmentée par récupération (RAG). Consultez la liste des modèles pour voir les modèles d’embedding disponibles et leur tarification.Documentation Index
Fetch the complete documentation index at: https://apidoc.cometapi.com/llms.txt
Use this file to discover all available pages before exploring further.
text-embedding-3-* prennent en charge le paramètre dimensions, qui raccourcit le vecteur d’embedding sans perte significative de précision. Cela peut réduire les coûts de stockage tout en conservant la majeure partie des informations sémantiques.input. Les entrées par lot sont nettement plus efficaces que l’envoi de requêtes individuelles.Autorisations
Bearer token authentication. Use your CometAPI key.
Corps
The embedding model to use. See the Models page for current embedding model IDs.
"text-embedding-3-small"
The text to embed. Can be a single string, an array of strings, or an array of token arrays. Each input must not exceed the model's maximum token limit (8,191 tokens for text-embedding-3-* models).
The format of the returned embedding vectors. float returns an array of floating-point numbers. base64 returns a base64-encoded string representation, which can reduce response size for large batches.
float, base64 The number of dimensions for the output embedding vector. Only supported by text-embedding-3-* models. Reducing dimensions can lower storage costs while maintaining most of the embedding's utility.
x >= 1A unique identifier for your end-user, which can help monitor and detect abuse.
Réponse
A list of embedding vectors for the input text(s).
The object type, always list.
list "list"
An array of embedding objects, one per input text. When multiple inputs are provided, results are returned in the same order as the input.
Show child attributes
Show child attributes
The model used to generate the embeddings.
"text-embedding-3-small"
Token usage statistics for this request.
Show child attributes
Show child attributes
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key="<COMETAPI_KEY>",
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="The food was delicious and the waiter was friendly.",
)
print(response.data[0].embedding[:5]) # First 5 dimensions
print(f"Dimensions: {len(response.data[0].embedding)}"){
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [
-0.0021,
-0.0491,
0.0209,
0.0314,
-0.0453
]
}
],
"model": "text-embedding-3-small",
"usage": {
"prompt_tokens": 2,
"total_tokens": 2
}
}