Skip to main content

🦜💪 Flex those feathers!

Project description

🚧 Under Active Development 🚧

🦜💪 LangChain Benchmarks

Release Notes CI License: MIT Twitter Open Issues

📖 Documentation

A package to help benchmark various LLM related tasks.

The benchmarks are organized by end-to-end use cases, and utilize LangSmith heavily.

We have several goals in open sourcing this:

  • Showing how we collect our benchmark datasets for each task
  • Showing what the benchmark datasets we use for each task is
  • Showing how we evaluate each task
  • Encouraging others to benchmark their solutions on these tasks (we are always looking for better ways of doing things!)

Installation

To install the packages, run the following command:

pip install -U langchain-benchmarks

All the benchmarks come with an associated benchmark dataset stored in LangSmith. To take advantage of the eval and debugging experience, sign up, and set your API key in your environment:

export LANGCHAIN_API_KEY=ls-...

Repo Structure

The package is located within langchain_benchmarks. Check out the docs for information on how to get starte.

The other directories are legacy and may be moved in the future.

Archived

Below are archived benchmarks that require cloning this repo to run.

Related

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

langchain_benchmarks-0.0.6.tar.gz (35.9 kB view details)

Uploaded Source

Built Distribution

langchain_benchmarks-0.0.6-py3-none-any.whl (52.5 kB view details)

Uploaded Python 3

File details

Details for the file langchain_benchmarks-0.0.6.tar.gz.

File metadata

  • Download URL: langchain_benchmarks-0.0.6.tar.gz
  • Upload date:
  • Size: 35.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for langchain_benchmarks-0.0.6.tar.gz
Algorithm Hash digest
SHA256 5aa369585f71d21794c11fa2fb69b63ec44c6fb7f7a46db142cd434210add4a7
MD5 6133864a5d71741805694770b8d2e962
BLAKE2b-256 77e6ad5bc3cac9723272a98146a8bd07892fa725f05040624fb6e198c05705fe

See more details on using hashes here.

File details

Details for the file langchain_benchmarks-0.0.6-py3-none-any.whl.

File metadata

File hashes

Hashes for langchain_benchmarks-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 19311518f8a5fd542c033f61f6e5c336f7b54580ae5b59eec9675106629ae513
MD5 86926d22545c5a4656751363c41e4112
BLAKE2b-256 adaed2d7c24bdde3a1784402bb8a0ebf0709472cbdcb724afee79f7737b7aff2

See more details on using hashes here.

Supported by

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