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BioLM Python client

Project description

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Python client and SDK for BioLM

Install the package:

pip install biolmai

Basic usage:

from biolmai 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 biolmai.client 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.

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