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POST
/
api
/
v1
/
beam
/
datasets
/
{dataset_id}
/
entries
Add entries
curl --request POST \
  --url https://api.example.com/api/v1/beam/datasets/{dataset_id}/entries

Documentation Index

Fetch the complete documentation index at: https://docs.allium.so/llms.txt

Use this file to discover all available pages before exploring further.

Adds values to a dataset. Duplicates are ignored. Changes are reflected immediately in beam.contains() lookups — no redeploy needed. Path parameters:
ParameterDescription
dataset_idDataset ID
Request body: 
{
  "values": ["0x3c499c542cef5e3811e1192ce70d8cc03d5c3359", "0xc2132d05d31c914a87c6611c10748aeb04b58e8f"]
}
FieldRequiredDescription
valuesYesArray of string values to add
curl -X POST https://api.allium.so/api/v1/beam/datasets/${DATASET_ID}/entries \
  -H "X-API-Key: ${ALLIUM_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{ "values": ["0x3c499c542cef5e3811e1192ce70d8cc03d5c3359"] }'
Response: 
{ "count": 1 }

​
Uploading large datasets

Each request supports up to 250,000 values. For larger datasets, split into chunks and upload in parallel. We recommend a maximum of 5 concurrent requests to avoid overloading the database: 
import asyncio
import httpx

CHUNK_SIZE = 250_000

async def upload_dataset(dataset_id: str, values: list[str], api_key: str):
    chunks = [values[i:i + CHUNK_SIZE] for i in range(0, len(values), CHUNK_SIZE)]
    semaphore = asyncio.Semaphore(5)  # max 5 concurrent requests

    async def upload_chunk(chunk: list[str]):
        async with semaphore:
            await client.post(
                f"https://api.allium.so/api/v1/beam/datasets/{dataset_id}/entries",
                json={"values": chunk},
                headers={"X-API-Key": api_key},
            )

    async with httpx.AsyncClient() as client:
        await asyncio.gather(*[upload_chunk(chunk) for chunk in chunks])
Use lowercase values when filtering by addresses — this is how values are normalized in the system.