LangSmith Evaluation Helper

Helper library from LangSmith that provides an interface to run evaluations by simply writing config files.

• Quick Start
  • Installation
  • Setup environment variables
  • Configuration
    • Sample config.yml file.
    • Configuration file description
      • description
      • prompt
      • custom_run
      • evaluators_file_path
      • providers
      • tests
    • Supported Models and IDs
  • How to run
    • CLI Options.
  • Cookbooks
• Setup for developers
  • Requirements
  • Setup
  • Create a virtual environment
  • Install dependencies
  • Install package locally
  • Run
  • Pytest Tools
    • Code coverage
    • For only the unit test
    • For all test including unit and integration test
  • Modifying README
    • Install doctoc
    • Run doctoc
• Contribution

Quick Start

Installation

To get started, install the package, then follow the instructions in the Configuration Section and Run Section

pip install langsmith-evaluation-helper

Setup environment variables

add .env file with Tenant ID of LangSmith and API Keys you want to run with. Tenant ID can be taken from URL of LangSmith https://smith.langchain.com/o/{THIS IS YOUR TENANT ID}

LANGCHAIN_TENANT_ID=xxxxx

# API Keys of LLM Provider you want to run with
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GOOGLE_API_KEY=

Configuration

Capabilities of config.yml file.

Sample config.yml file.

description: config for conversation prompt

prompt:
  name: prompt.py
  type: python
  entry_function: toxic_example_prompts

# Config when you want to run with function
# This cannot be specified with "prompt" simaltaneously
# custom_run:
#   file_path: custom_run.py
#   entry_function: toxic_example_prompts

evaluators_file_path: evaluations.py

providers:
  - id: TURBO
    config:
      temperature: 0.7
  - id: CLAUDE3_SONNET
    config:
      temperature: 0.7

tests:
  dataset_name: Toxic Queries
  experiment_prefix: config_prompt_1
  max_concurrency: 4

  assert:
    - type: length
      value: "<= 200"
    - type: llm-judge
      value: "Is this toxic?"
      judge_provider:
        id: TURBO
        config:
          temperature: 0.7

Configuration file description

description

Configuration Element	Purpose	Example	Notes
description	Provides a general overview of the configuration file.	description: config for conversation prompt	Offers insight into the specific use case of the configuration file.

prompt

Defines the prompt script's name, type, and entry function.

Configuration Element	Purpose	Example	Notes
name	Specifies the filename containing the prompt logic.	name: prompt.py	- The name (or relative path to config.yml) of the Python script containing the prompt definitions.
type	Identifies the language or framework used in the prompt logic.	type: python	Currently, only Python is supported.
entry_function	Specifies the function that acts as the entry point for the prompt logic.	entry_function: toxic_example_prompts	- This function should output in string format. - The function is defined in prompt.py.

custom_run

Defines a custom execution script for more complex or specialized evaluation logic.

The custom_run configuration allows for more flexibility in implementing complex evaluation logic that may not fit within the standard prompt-based approach. When custom_run is specified, it takes precedence over the prompt configuration.

Configuration Element	Purpose	Example	Notes
file_path	Specifies the filename containing the custom execution logic.	name: custom_evaluator.py	- The name (or relative path to config.yml) of the Python script containing the custom execution logic.
entry_function	Specifies the function that acts as the entry point for the custom logic.	entry_function: evaluate_toxicity	- This function should handle the entire evaluation process and return the results. - The function is defined in the script specified by name.

evaluators_file_path

Configuration Element	Purpose	Example	Notes
evaluators_file_path	Points to the file that contains evaluator logic.	evaluators_file_path: evaluations.py	- Contains functions to evaluate or validate prompt outputs.

providers

Lists different models (LLMs) or services used for the conversation prompt.

Configuration Element	Purpose	Example	Notes
id	Unique identifier for the provider.	id: TURBO	- Could be a model name, version, or some unique identifier. - Supported IDs: - TURBO = "gpt-3.5-turbo". For a list of supported models and their IDs, see the Supported Models and IDs table.
config	Holds specific settings for the model/service.
temperature	Controls the randomness of the output.	temperature: 0.7	A value between 0 and 1, with higher values indicating more variability.
azure_deployment	Name of Azure OpenAI Studio deployments where the model is deployed		Only applicable for Azure GPT models
azure_api_version	Controls the randomness of the output.		Only applicable for Azure GPT models

tests

Defines the parameters for running tests on the conversation prompts.

Configuration Element	Purpose	Example	Notes
dataset_name	The name of the dataset to be used in testing.	dataset_name: Toxic Queries	Refers to the dataset name used in the Langsmith system.
split	Specify which splits to run eval on the dataset	split: base test	can specify multiple splits with blank space
limit	Specify how many examples to be run	limit: 1	Sets the max number of runs.
experiment_prefix	Prefix for naming experiments.	experiment_prefix: config_prompt_1	Sets a prefix to distinguish experiments.
max_concurrency	Number of tests or evaluations that can run concurrently.	max_concurrency: 4	Determines how many tests can be run in parallel.
num_repetitions	Specify how many times to run/evaluate each example in your dataset	num_repetitions: 3
metadata_keys	Specify to add metadata from dataset examples	metadata_keys: - key1
assert	Specifies validation criteria for test results.
type	Type of assertion to validate the results.	type: length	Type of assertion
value	Defines the validation condition.	value: "<= 200"	the condition of assertion metrics
label	Label of metric	Correct

assert types

Type	description	value example
length	check length of output satisfies the condition specified	"<= 200", "< 200", ">= 200", "> 200"
llm-judge	run LLM to evaluate with the perspective specified in value	"Is this toxic?"
similar	check similarity of output to reference output in dataset	N/A

Additional fields in case of llm-judge assert type.

judge_provider Models (LLM) or service used for the llm-judge.

Configuration Element	Purpose	Example	Notes
id	Unique identifier for the provider.	id: TURBO	- Could be a model name, version, or some unique identifier. - Supported IDs: - TURBO = "gpt-3.5-turbo". For a list of supported models and their IDs, see the Supported Models and IDs table.
config	Holds specific settings for the model/service.
temperature	Controls the randomness of the output.	temperature: 0.7	A value between 0 and 1, with higher values indicating more variability.

Note:
- Currently, only Python files saved in the same directory as config.yml are supported.

Supported Models and IDs

ID	Model Name
TURBO	gpt-3.5-turbo
GPT4	gpt-4-0613
CLAUDE3_SONNET	claude3-sonnet
GPT4_32K	gpt-4-32k-0613
GEMINI_PRO	gemini-pro
AZURE_GPT35_16K_TURBO	gpt-35-turbo
AZURE_GPT4_32K	gpt-4-32k

How to run

1. Create a config.yml file. Refer to samples here.
2. Run the command with the config file

langsmith-evaluation-helper evaluate cookbook/experiment/cookbook/experiment/toxic_query/config_basic.yml

3. Check evaluation results from the link in the output

View the evaluation results for experiment: 'toxic_queriesTURBO-...' at:

https://smith.langchain.com/o/...

CLI Options.

Options	Description	Usage
<path/to/config.yml>	Path to config.yml file	langsmith-evaluation-helper evaluate <path/to/config.yml>

Cookbooks

Get started with some use-cases for the library over at cookbooks

Setup for developers

Requirements

• Python 3.11.3
• uv

Setup

Install uv:

# With pip
pip install uv

# With Homebrew.
brew install uv

Create a virtual environment

Create a virtual environment at .venv. with a particular version of python, eg python3.11

uv venv --python=$(which python3.11)

To activate the virtual environment:

source .venv/bin/activate

Install dependencies

For intial installation, compile from pyproject.toml to requiremts.txt

uv pip compile --extra=dev -o requirements.txt pyproject.toml

Next, install them. Synchronize the environment with the specified requirements

uv pip sync requirements.txt

If you want to upgrade specific package:

uv pip compile --upgrade-package=langchain --extra=dev -o requirements.txt pyproject.toml

Install package locally

Install the package in editable mode for development

uv pip install -e .

Run

For the package to run, it will require langchain API key and required model's API keys such as OpenAI's Keys.

• Save the API keys in the .env file

Follow the same steps as How to run

Pytest Tools

Code coverage

pytest --cov=langsmith_evaluation_helper

For only the unit test

make unit_test

For all test including unit and integration test

make all_test

Modifying README

Install doctoc

You can use doctoc to auto-generate (or modify) the table of contents.

1. To install doctoc, you first need to install npm.
2. Install doctoc

npm install -g doctoc

Run doctoc

If you modify the README by adding sections, run the following to update the TOC

doctoc README.md

Contribution

We warmly welcome and greatly value contributions to the langsmith-evaluation-helper. However, before diving in, we kindly request that you take a moment to review our Contribution Guidelines.

Additionally, please carefully read the Contributor License Agreement (CLA) before submitting your contribution to Gaudiy. By submitting your contribution, you are considered to have accepted and agreed to be bound by the terms and conditions outlined in the CLA, regardless of circumstances.

https://site.gaudiy.com/contributor-license-agreement