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-0.3.2.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-0.3.2-py3-none-any.whl (9.4 kB view details)

Uploaded Python 3

File details

Details for the file async_llms-0.3.2.tar.gz.

File metadata

  • Download URL: async_llms-0.3.2.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-0.3.2.tar.gz
Algorithm Hash digest
SHA256 b24779ef18e069115ed68e88ec83368fd32c9ffe3c60e3328a890a97b752430f
MD5 7cf49b0e1a67e3715fef095729fb8ff7
BLAKE2b-256 8522759566b5c7671d1d990efb3b18159af5ee6ee11b1eb1e11ca4080aa645c3

See more details on using hashes here.

File details

Details for the file async_llms-0.3.2-py3-none-any.whl.

File metadata

  • Download URL: async_llms-0.3.2-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-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 521c9c353023acba8e33f4b854a0ffb8ef479469f96c29d684b4330ce20f9e11
MD5 60aa6be7a8f9c12093171d1901420832
BLAKE2b-256 f1fac164a485e11ba7e9a0099abe6b10514737f1537335391187642f033ff0ea

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