BenchLLM

What is BenchLLM?

BenchLLM is a tool designed to evaluate the performance and quality of applications powered by Large Language Models (LLMs). It provides a flexible and comprehensive framework for building test suites, generating quality reports, and monitoring model performance. Whether you need automated, interactive, or custom evaluation strategies, BenchLLM offers the features and capabilities to ensure your AI models meet the required standards.

How does BenchLLM work?

BenchLLM works by allowing users to define tests, run models against those tests, and then evaluate the results. Here’s a detailed breakdown:

1. Define Tests Intuitively: Tests can be defined in JSON or YAML format, making it easy to set up and manage test cases.
2. Organize Tests into Suites: Organize tests into suites for easy versioning and management. This helps in maintaining different versions of tests as the models evolve.
3. Run Tests: Use the powerful CLI or flexible API to run tests against your models. BenchLLM supports OpenAI, Langchain, and any other API out of the box.
4. Evaluate Results: BenchLLM provides multiple evaluation strategies to assess the performance of your models. It helps identify regressions in production and monitor model performance over time.
5. Generate Reports: Generate evaluation reports and share them with your team. These reports provide insights into the strengths and weaknesses of your models.

Example Code Snippets:

Here’s an example of how to use BenchLLM with Langchain:

from benchllm import SemanticEvaluator, Test, Tester
from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI

## Keep your code organized in the way you like
def run_agent(input: str):
    llm=OpenAI(temperature=0)
    agent = initialize_agent(
        load_tools(["serpapi", "llm-math"], llm=llm),
        llm=llm,
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
    )
    return agent(input)["output"]

## Instantiate your Test objects
tests = [
    Test(
        input="When was V7 founded? Divide it by 2",
        expected=["1009", "That would be 2018 / 2 = 1009"]
    )
]

## Use a Tester object to generate predictions
tester = Tester(run_agent)
tester.add_tests(tests)
predictions = tester.run()

## Use an Evaluator object to evaluate your model
evaluator = SemanticEvaluator(model="gpt-3")
evaluator.load(predictions)
evaluator.run()

Here’s an example of how to use BenchLLM with OpenAI’s ChatCompletion API:

import benchllm
from benchllm.input_types import ChatInput
import openai

def chat(messages: ChatInput):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages
    )
    return response.choices[0].message.content.strip()

@benchllm.test(suite=".")
def run(input: ChatInput):
    return chat(input)

Key Features and Benefits

• Flexible API: Test code on the fly with support for OpenAI, Langchain, and other APIs.
• Powerful CLI: Run and evaluate models with simple CLI commands, ideal for CI/CD pipelines.
• Easy Evaluation: Define tests intuitively in JSON or YAML format.
• Automation: Automate evaluations within a CI/CD pipeline to ensure continuous quality.
• Insightful Reports: Generate and share evaluation reports to monitor model performance.
• Performance Monitoring: Detect regressions in production by monitoring model performance.

How to use BenchLLM?

1. Installation: Download and install BenchLLM.
2. Define Tests: Create test suites in JSON or YAML.
3. Run Evaluations: Use the CLI or API to run tests against your LLM applications.
4. Analyze Reports: Review the generated reports to identify areas for improvement.

Who is BenchLLM for?

BenchLLM is designed for AI engineers and developers who want to ensure the quality and reliability of their LLM-powered applications. It is particularly useful for:

• AI Engineers: Those building and maintaining AI products.
• Developers: Integrating LLMs into their applications.
• Teams: Looking to monitor and improve the performance of their AI models.

Why choose BenchLLM?

BenchLLM provides a comprehensive solution for evaluating LLM applications, offering flexibility, automation, and insightful reporting. It is built by AI engineers who understand the need for powerful and flexible tools that deliver predictable results. By using BenchLLM, you can:

• Ensure the quality of your LLM applications.
• Automate the evaluation process.
• Monitor model performance and detect regressions.
• Improve collaboration with insightful reports.

By choosing BenchLLM, you are opting for a robust and reliable solution to evaluate your AI models and ensure they meet the highest standards of performance and quality.