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90 days of the enterprise, pro and team plans free, no card needed. See the plans
embed-small
Text embeddings for search, clustering and retrieval; answers the OpenAI-compatible /v1/embeddings endpoint (never chat).
Requests per day this target answered on this deployment, with the cost line.
No requests yet. Route something from the playground to see usage here.
Domains the signal layer detected on requests that ended here.
No routed requests yet. Declared domains: General.
Hard stops enforced before scoring.
This llm is not executed by the platform. Ask the router for a decision restricted to kinds=["llm"] and dispatch the winner in your own stack; send feedback afterwards so the learners improve.
curl https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Embed these product descriptions for semantic search.", "kinds": ["llm"], "top_k": 3}'
# Returns the chosen target, confidence, the ranked alternatives and the
# per-strategy trace. Execution of llm targets happens in your stack.import httpx
r = httpx.post(
"https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route",
headers={"Authorization": "Bearer osr_live_..."},
json={"text": "Embed these product descriptions for semantic search.", "kinds": ["llm"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]]) # e.g. embed-small first when it fitsPhoto: Varun Bhatheja
Examples the similarity strategy matches against.
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
Free plan, no card. Fifteen thousand decisions a month with the full trace.
