fore

KO | EN

gitlite — search

#hacktoberfest #ai #golang #android #machine-learning #rust #macos #windows #awesome #cli #kubernetes #vue #openai #mcp

(★ 13)

The fore client package

fore Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# The fore client package

The foresight library within fore SDK allows you to easily evaluate the
performance of your LLM system based on a variety of metrics.

You can sign-up and get started immediately at https://foresight.foreai.co.

Check our documentation at https://docs.foreai.co.

## Quick start
1.  Install the package using `pip`:
    ```bash
    pip install fore
    ```
    Or download the repo from [GitHub](https://github.com/foreai-co/fore/) and install via `pip install .`

2.
    - Get started with the following lines:
    ```python
    from fore.foresight import Foresight

foresight = Foresight(api_token="<YOUR_API_TOKEN>")

foresight.log(query="What is the easiest programming language?",
                  response="Python",
                  contexts=["Python rated the easiest programming language"],
                  tag="my_awesome_experiment")
    
    # You can add more such queries using foresight.log
    # ....

foresight.flush()
    ```

- Or alternatively to curate your evalsets and run regular evals against them do:
    ```python
    from fore.foresight import EvalRunConfig, Foresight, InferenceOutput, MetricType

foresight = Foresight(api_token="<YOUR_API_TOKEN>")

evalset = foresight.create_simple_evalset(
        evalset_id="programming-languages",
        queries=["hardest programming language?", "easiest programming language?"],
        reference_answers=["Malbolge", "Python"])

run_config = EvalRunConfig(evalset_id="programming-languages",
                            experiment_id="my-smart-llm",
                            metrics=[MetricType.GROUNDEDNESS, 
                                     MetricType.REFERENCE_FACT_RECALL])

def my_generate_fn(query: str) -> InferenceOutput:
        # Do the LLM processing with your model...
        # Here is some demo code:
        return InferenceOutput(
            generated_response="Malbolge" if "hardest" in query else "Python",
            contexts=[
                "Malbolge is the hardest language", "Python is the easiest language"
            ])

foresight.generate_answers_and_run_eval(my_generate_fn, run_config)
    ```

## Metrics

We currently offer two metrics:
* [Groundedness](https://docs.foreai.co/docs/foresight/metrics/groundedness)
* [Reference Fact Recall](https://docs.foreai.co/docs/foresight/metrics/reference_fact_recall)

Check [here](https://docs.foreai.co/docs/category/metrics) for more information.

fore

fore Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

fore

fore Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README