Models / Embeddings / BGE-Large-EN v1.5
BGE-Large-EN v1.5
Embeddings
BAAI v1.5 large maps text to dense vectors for retrieval, classification, clustering, semantic search, and LLM databases.
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API Usage
Endpoint
BAAI/bge-large-en-v1.5
RUN INFERENCE
curl -X POST "https://api.together.xyz/v1/embeddings" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d {
"model": "BAAI/bge-large-en-v1.5",
"input": "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
}'JSON RESPONSE
RUN INFERENCE
from together import Together
client = Together()
response = client.embeddings.create(
model="BAAI/bge-large-en-v1.5",
input="Our solar system orbits the Milky Way galaxy at about 515,000 mph",
)
print(response.data[0].embedding)JSON RESPONSE
RUN INFERENCE
import Together from "together-ai";
const together = new Together();
const response = await together.embeddings.create({
model: "BAAI/bge-large-en-v1.5",
input: "Our solar system orbits the Milky Way galaxy at about 515,000 mph",
});
console.log(response.data[0].embedding);JSON RESPONSE
Model Provider:
BAAI
Type:
Embeddings
Variant:
Large
Parameters:
335M
Deployment:
✔ Serverless
Quantization
Context length:
512
Pricing:
$0.02
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Quickstart docs
Looking for production scale? Deploy on a dedicated endpoint
Deploy BGE-Large-EN v1.5 on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.
