BGE Base EN v1.5
lx1-bge-base-enEmbeddingsLive in the catalogThe balanced English embedding most retrieval stacks start with: 768 dimensions is wide enough to hold real semantic nuance and narrow enough to keep an index small and searches fast. If you are adding retrieval to a product and do not yet have a reason to choose otherwise, start here.
- 01768 dimensions — the common default width
- 02Balanced accuracy against index size
- 03Also reachable as lx1-embed
Turns text into a fixed-length vector for semantic search, RAG, clustering, and dedupe. Call it on /v1/embeddings with a string or array of inputs — no chat, tools, or reasoning.
One endpoint. Served by our engine.
BGE Base EN v1.5 is served through the Layer X1 engine — zero-downtime serving is the design target, not a status-page apology. You request it by name; everything else is our problem.
curl https://api.layerx1.com/v1/embeddings \
-H "authorization: Bearer lx1_your_key" \
-H "content-type: application/json" \
-d '{
"model": "lx1-bge-base-en",
"input": "text to embed"
}'OpenAI-style clients work too — POST the same model name to /v1/embeddings with a Bearer key. See the docs for both dialects.
Your agent stays the same.
Its inference gets better.
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