biolm-sdk 0.0.1

BioLM Python client for the hosted platform and biolm-hub

Python client and SDK for BioLM

Install the package:

pip install biolm

Open-source models (biolm-hub):

# In biolm-hub: bh serve
biolm hub set
biolm model list
biolm model run esm2-8m encode -i seq.json

See docs/cli/hub.rst.

Basic usage:

from biolm import biolm

# Encode a single sequence
result = biolm(entity="esm2-8m", action="encode", type="sequence", items="MSILVTRPSPAGEEL")

# Predict a batch of sequences
result = biolm(entity="esmfold", action="predict", type="sequence", items=["SEQ1", "SEQ2"])

# Write results to disk
biolm(entity="esmfold", action="predict", type="sequence", items=["SEQ1", "SEQ2"], output='disk', file_path="results.jsonl")

Asynchronous usage:

from biolm.core.http import BioLMApiClient
import asyncio

async def main():
    model = BioLMApiClient("esmfold")
    result = await model.predict(items=[{"sequence": "MDNELE"}])
    print(result)

asyncio.run(main())

Overview

The BioLM Python client provides a high-level, user-friendly interface for interacting with the BioLM API. It supports both synchronous and asynchronous usage, automatic batching, flexible error handling, and efficient processing of biological data.

Main features:

• High-level BioLM constructor for quick requests

• Sync and async interfaces

• Automatic or custom rate limiting/throttling

• Schema-based batch size detection

• Flexible input formats (single key + list, or list of dicts)

• Low memory usage via generators

• Flexible error handling (raise, continue, or stop on error)

• Universal HTTP client for both sync and async

Features

• High-level constructor: Instantly run an API call with a single line.

• Sync and async: Use BioLM for sync, or BioLMApiClient for async.

• Flexible rate limiting: Use API throttle, disable, or set your own (e.g., ‘1000/second’).

• Schema-based batching: Automatically queries API for max batch size.

• Flexible input: Accepts a single key and list, or list of dicts, or list of lists for advanced batching.

• Low memory: Uses generators for validation and batching.

• Error handling: Raise HTTPX errors, continue on error, or stop on first error.

• Disk output: Write results as JSONL to disk.

• Universal HTTP client: Efficient for both sync and async.

• Direct access to schema and batching: Use BioLMApi for advanced workflows, including .schema(), .call(), and ._batch_call_autoschema_or_manual().

Example endpoints and actions:

• esm2-8m/encode: Embedding for protein sequences.

• esmfold/predict: Structure prediction for protein sequences.

• progen2-oas/generate: Sequence generation from a context string.

• dnabert2/predict: Masked prediction for protein sequences.

• ablang2/encode: Embeddings for paired-chain antibodies.

• Free software: Apache Software License 2.0

• Documentation: https://docs.biolm.ai