Skip to main content

A Python library for making asynchronous LLM calls

Project description

async-llms: A Python Library for Asynchronous LLM Calls

async-llms is a Python library for making asynchronous LLM calls to accelerate LLM evaluation experiments.

Installation

You can install the package using pip:

pip install async-llms

Usage

Set API Key

Set the API key for the LLM provider you want to use.

export OPENAI_API_KEY="your_api_key"
export GOOGLE_API_KEY="your_api_key"
export XAI_API_KEY="your_api_key"

If you are using Google Vertex AI, you can set the project and location using the GOOGLE_PROJECT and GOOGLE_LOCATION environment variables.

export USE_VERTEX_AI="True"  # default is False
export GOOGLE_PROJECT="your_project"
export GOOGLE_LOCATION="your_location"  # "us-central1"

(Optional) Set Timeout

You can set the timeout for the LLM calls using the ASYNC_LLM_TIMEOUT environment variable.

export ASYNC_LLM_TIMEOUT=1800  # 1800 seconds per request

Command Line Interface

You can use the package directly from the command line:

async-llms \
    --api_type "openai" \
    --input_jsonl "path/to/input.jsonl" \
    --output_jsonl "path/to/output.jsonl" \
    --num_parallel_tasks "num_parallel_tasks"

Input Format

The input JSONL file format is identical to the one used in OpenAI's Batch API: https://platform.openai.com/docs/guides/batch

{
    "custom_id": "unique_id_for_this_request",
    "body": {
        // Your LLM request parameters here
    }
}

License

MIT License

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

async_llms-0.3.3.tar.gz (8.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

async_llms-0.3.3-py3-none-any.whl (9.4 kB view details)

Uploaded Python 3

File details

Details for the file async_llms-0.3.3.tar.gz.

File metadata

  • Download URL: async_llms-0.3.3.tar.gz
  • Upload date:
  • Size: 8.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.16

File hashes

Hashes for async_llms-0.3.3.tar.gz
Algorithm Hash digest
SHA256 e94c04cfa90d82c04836e6ded4ebc60d7cc4e4661dde7dab6903030a03f792cc
MD5 5b3a4565e2bb2263f0ddc6e053ca7e90
BLAKE2b-256 8171e6fab352a2cf7a7a5e66ea1b47682157fa2a97519ea6b79cc8fb6abec0a0

See more details on using hashes here.

File details

Details for the file async_llms-0.3.3-py3-none-any.whl.

File metadata

  • Download URL: async_llms-0.3.3-py3-none-any.whl
  • Upload date:
  • Size: 9.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.16

File hashes

Hashes for async_llms-0.3.3-py3-none-any.whl
Algorithm Hash digest
SHA256 5b1f5eb7a0692b93dd2641f9829f40b3b7688348a16861be67f3f67681518d37
MD5 9c10f287f3a9311a4199cd4d3e45686e
BLAKE2b-256 c736d9e9c01b3c181a73c808e63d13791106f8682b7633054ac456088990cb9a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page