| Name | flow-judge JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | A small yet powerful LM Judge |
| upload_time | 2024-10-29 07:32:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
lm-judge
evaluation
llms
ai
benchmarking
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# `flow-judge`
<p align="center">
<img src="img/flow_judge_banner.png" alt="Flow Judge Banner">
</p>
<p align="center" style="font-family: 'Courier New', Courier, monospace;">
<strong>
<a href="https://www.flow-ai.com/judge">Technical Report</a> |
<a href="https://huggingface.co/collections/flowaicom/flow-judge-v01-66e6af5fc3b3a128bde07dec">Model Weights</a> |
<a href="https://huggingface.co/spaces/flowaicom/Flow-Judge-v0.1">HuggingFace Space</a> |
<a href="https://github.com/flowaicom/lm-evaluation-harness/tree/Flow-Judge-v0.1_evals/lm_eval/tasks/flow_judge_evals">Evaluation Code</a> |
<a href="https://github.com/flowaicom/flow-judge/tree/main/examples">Tutorials</a>
</strong>
</p>
<p align="center" style="font-family: 'Courier New', Courier, monospace;">
<code>flow-judge</code> is a lightweight library for evaluating LLM applications with <code>Flow-Judge-v0.1</code>.
</p>
<p align="center">
<a href="https://github.com/flowaicom/flow-judge/stargazers/" target="_blank">
<img src="https://img.shields.io/github/stars/flowaicom/flow-judge?style=social&label=Star&maxAge=3600" alt="GitHub stars">
</a>
<a href="https://github.com/flowaicom/flow-judge/releases" target="_blank">
<img src="https://img.shields.io/github/v/release/flowaicom/flow-judge?color=white" alt="Release">
</a>
<a href="https://www.youtube.com/@flowaicom" target="_blank">
<img alt="YouTube Channel Views" src="https://img.shields.io/youtube/channel/views/UCo2qL1nIQRHiPc0TF9xbqwg?style=social">
</a>
<a href="https://github.com/flowaicom/flow-judge/actions/workflows/test-and-lint.yml" target="_blank">
<img src="https://github.com/flowaicom/flow-judge/actions/workflows/test-and-lint.yml/badge.svg" alt="Build">
</a>
<a href="https://codecov.io/gh/flowaicom/flow-judge" target="_blank">
<img src="https://codecov.io/gh/flowaicom/flow-judge/branch/main/graph/badge.svg?token=AEGC7W3DGE" alt="Code coverage">
</a>
<a href="https://github.com/flowaicom/flow-judge/blob/main/LICENSE" target="_blank">
<img src="https://img.shields.io/static/v1?label=license&message=Apache%202.0&color=white" alt="License">
</a>
<a href="https://app.fossa.com/projects/git%2Bgithub.com%2Fflowaicom%2Fflow-judge?ref=badge_shield" alt="FOSSA Status"><img src="https://app.fossa.com/api/projects/git%2Bgithub.com%2Fflowaicom%2Fflow-judge.svg?type=shield"/></a>
</p>
## Model
`Flow-Judge-v0.1` is an open, small yet powerful language model evaluator trained on a synthetic dataset containing LLM system evaluation data by Flow AI.
You can learn more about the unique features of our model in the [technical report](https://www.flow-ai.com/blog/flow-judge#flow-judge-an-open-small-language-model-for-llm-system-evaluations).
## Features of the library
- Support for multiple model types: Hugging Face Transformers and vLLM
- Extensible architecture for custom metrics
- Pre-defined evaluation metrics
- Ease of custom metric and rubric creation
- Batched evaluation for efficient processing
- Integrations with most popular frameworks like Llama Index
## Installation
Install flow-judge using pip:
```bash
pip install -e ".[vllm,hf]"
pip install 'flash_attn>=2.6.3' --no-build-isolation
```
Extras available:
- `dev` to install development dependencies
- `hf` to install Hugging Face Transformers dependencies
- `vllm` to install vLLM dependencies
- `llamafile` to install Llamafile dependencies
- `baseten` to install Baseten dependencies
## Quick Start
Here's a simple example to get you started:
```python
from flow_judge import Vllm, Llamafile, Hf, EvalInput, FlowJudge
from flow_judge.metrics import RESPONSE_FAITHFULNESS_5POINT
from IPython.display import Markdown, display
# If you are running on an Ampere GPU or newer, create a model using VLLM
model = Vllm()
# If you have other applications open taking up VRAM, you can use less VRAM by setting gpu_memory_utilization to a lower value.
# model = Vllm(gpu_memory_utilization=0.70)
# Or if not running on Ampere GPU or newer, create a model using no flash attn and Hugging Face Transformers
# model = Hf(flash_attn=False)
# Or create a model using Llamafile if not running an Nvidia GPU & running a Silicon MacOS for example
# model = Llamafile()
# Initialize the judge
faithfulness_judge = FlowJudge(
metric=RESPONSE_FAITHFULNESS_5POINT,
model=model
)
# Sample to evaluate
query = """..."""
context = """...""""
response = """..."""
# Create an EvalInput
# We want to evaluate the response to the customer issue based on the context and the user instructions
eval_input = EvalInput(
inputs=[
{"query": query},
{"context": context},
],
output={"response": response},
)
# Run the evaluation
result = faithfulness_judge.evaluate(eval_input, save_results=False)
# Display the result
display(Markdown(f"__Feedback:__\n{result.feedback}\n\n__Score:__\n{result.score}"))
```
## Usage
### Inference Options
The library supports multiple inference backends to accommodate different hardware configurations and performance needs:
1. **vLLM**:
- Best for NVIDIA GPUs with Ampere architecture or newer (e.g., RTX 3000 series, A100, H100)
- Offers the highest performance and throughput
- Requires CUDA-compatible GPU
```python
from flow_judge import Vllm
model = Vllm()
```
2. **Hugging Face Transformers**:
- Compatible with a wide range of hardware, including older NVIDIA GPUs
- Supports CPU inference (slower but universally compatible)
- It is slower than vLLM but generally compatible with more hardware.
If you are running on an Ampere GPU or newer:
```python
from flow_judge import Hf
model = Hf()
```
If you are not running on an Ampere GPU or newer, disable flash attention:
```python
from flow_judge import Hf
model = Hf(flash_attn=False)
```
3. **Llamafile**:
- Ideal for non-NVIDIA hardware, including Apple Silicon
- Provides good performance on CPUs
- Self-contained, easy to deploy option
```python
from flow_judge import Llamafile
model = Llamafile()
```
4. **Baseten**:
- Remote execution.
- Machine independent.
- Improved concurrency patterns for larger workloads.
```python
from flow_judge import Baseten
model = Baseten()
```
For detailed information on using Baseten, visit the [Baseten readme](https://github.com/flowaicom/flow-judge/blob/feat/baseten-integration/flow_judge/models/adapters/baseten/README.md).
Choose the inference backend that best matches your hardware and performance requirements. The library provides a unified interface for all these options, making it easy to switch between them as needed.
### Evaluation Metrics
`Flow-Judge-v0.1` was trained to handle any custom metric that can be expressed as a combination of evaluation criteria and rubric, and required inputs and outputs.
#### Pre-defined Metrics
For convenience, `flow-judge` library comes with pre-defined metrics such as `RESPONSE_CORRECTNESS` or `RESPONSE_FAITHFULNESS`. You can check the full list by running:
```python
from flow_judge.metrics import list_all_metrics
list_all_metrics()
```
### Batched Evaluations
For efficient processing of multiple inputs, you can use the `batch_evaluate` method:
```python
# Read the sample data
import json
from flow_judge import Vllm, EvalInput, FlowJudge
from flow_judge.metrics import RESPONSE_FAITHFULNESS_5POINT
from IPython.display import Markdown, display
# Initialize the model
model = Vllm()
# Initialize the judge
faithfulness_judge = FlowJudge(
metric=RESPONSE_FAITHFULNESS_5POINT,
model=model
)
# Load some sampledata
with open("sample_data/csr_assistant.json", "r") as f:
data = json.load(f)
# Create a list of inputs and outputs
inputs_batch = [
[
{"query": sample["query"]},
{"context": sample["context"]},
]
for sample in data
]
outputs_batch = [{"response": sample["response"]} for sample in data]
# Create a list of EvalInput
eval_inputs_batch = [EvalInput(inputs=inputs, output=output) for inputs, output in zip(inputs_batch, outputs_batch)]
# Run the batch evaluation
results = faithfulness_judge.batch_evaluate(eval_inputs_batch, save_results=False)
# Visualizing the results
for i, result in enumerate(results):
display(Markdown(f"__Sample {i+1}:__"))
display(Markdown(f"__Feedback:__\n{result.feedback}\n\n__Score:__\n{result.score}"))
display(Markdown("---"))
```
## Advanced Usage
> [!WARNING]
> There exists currently a reported issue with Phi-3 models that produces gibberish outputs with contexts longer than 4096 tokens, including input and output. This issue has been recently fixed in the transformers library so we recommend using the `Hf()` model configuration for longer contexts at the moment. For more details, refer to: [#33129](https://github.com/huggingface/transformers/pull/33129) and [#6135](https://github.com/vllm-project/vllm/issues/6135)
### Custom Metrics
Create your own evaluation metrics:
```python
from flow_judge.metrics import CustomMetric, RubricItem
custom_metric = CustomMetric(
name="My Custom Metric",
criteria="Evaluate based on X, Y, and Z.",
rubric=[
RubricItem(score=0, description="Poor performance"),
RubricItem(score=1, description="Good performance"),
],
required_inputs=["query"],
required_output="response"
)
judge = FlowJudge(metric=custom_metric, config="Flow-Judge-v0.1-AWQ")
```
### Integrations
We support an integration with Llama Index evaluation module and Haystack:
- [Llama Index tutorial](https://github.com/flowaicom/flow-judge/blob/main/examples/4_llama_index_evaluators.ipynb)
- [Haystack tutorial](https://github.com/flowaicom/flow-judge/blob/main/examples/5_evaluate_haystack_rag_pipeline.ipynb)
> Note that we are currently working on adding more integrations with other frameworks in the near future.
## Development Setup
1. Clone the repository:
```bash
git clone https://github.com/flowaicom/flow-judge.git
cd flow-judge
```
2. Create a virtual environment:
```bash
virtualenv ./.venv
```
or
```bash
python -m venv ./.venv
```
3. Activate the virtual environment:
- On Windows:
```bash
venv\Scripts\activate
```
- On macOS and Linux:
```bash
source venv/bin/activate
```
4. Install the package in editable mode with development dependencies:
```bash
pip install -e ".[dev]"
```
or
```bash
pip install -e ".[dev,vllm]"
```
for vLLM support.
5. Set up pre-commit hooks:
```bash
pre-commit install
```
6. Make sure you have trufflehog installed:
```bash
# make trufflehog available in your path
# macos
brew install trufflehog
# linux
curl -sSfL https://raw.githubusercontent.com/trufflesecurity/trufflehog/main/scripts/install.sh | sh -s -- -b /usr/local/bin
# nix
nix profile install nixpkgs#trufflehog
```
7. Run pre-commit on all files:
```bash
pre-commit run --all-files
```
8. You're now ready to start developing! You can run the main script with:
```bash
python -m flow_judge
```
Remember to always activate your virtual environment when working on the project. To deactivate the virtual environment when you're done, simply run:
```bash
deactivate
```
## Running Tests
To run the tests for Flow-Judge, follow these steps:
1. Navigate to the root directory of the project in your terminal.
2. Run the tests using pytest:
```bash
pytest tests/
```
This will discover and run all the tests in the `tests/` directory.
3. If you want to run a specific test file, you can do so by specifying the file path:
```bash
pytest tests/test_flow_judge.py
```
4. For more verbose output, you can use the `-v` flag:
```bash
pytest -v tests/
```
## Contributing
Contributions to `flow-judge` are welcome! Please follow these steps:
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
Please ensure that your code adheres to the project's coding standards and passes all tests.
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fflowaicom%2Fflow-judge?ref=badge_large)
## Acknowledgments
Flow-Judge is developed and maintained by the Flow AI team. We appreciate the contributions and feedback from the AI community in making this tool more robust and versatile.
Raw data
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"keywords": "LM-judge, evaluation, LLMs, AI, benchmarking",
"author": null,
"author_email": "Bernardo Garcia <bernardo@flow-ai.com>, Karolus Sariola <karolus@flow-ai.com>, Minaam Shahid <minaam@flow-ai.com>, Tiina Vaahtio <tiina@flow-ai.com>, Alex Wegrzyn <alex.wegrzyn@flow-ai.com>",
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"description": "# `flow-judge`\n\n<p align=\"center\">\n <img src=\"img/flow_judge_banner.png\" alt=\"Flow Judge Banner\">\n</p>\n\n<p align=\"center\" style=\"font-family: 'Courier New', Courier, monospace;\">\n <strong>\n <a href=\"https://www.flow-ai.com/judge\">Technical Report</a> |\n <a href=\"https://huggingface.co/collections/flowaicom/flow-judge-v01-66e6af5fc3b3a128bde07dec\">Model Weights</a> |\n <a href=\"https://huggingface.co/spaces/flowaicom/Flow-Judge-v0.1\">HuggingFace Space</a> |\n <a href=\"https://github.com/flowaicom/lm-evaluation-harness/tree/Flow-Judge-v0.1_evals/lm_eval/tasks/flow_judge_evals\">Evaluation Code</a> |\n <a href=\"https://github.com/flowaicom/flow-judge/tree/main/examples\">Tutorials</a>\n </strong>\n</p>\n\n<p align=\"center\" style=\"font-family: 'Courier New', Courier, monospace;\">\n <code>flow-judge</code> is a lightweight library for evaluating LLM applications with <code>Flow-Judge-v0.1</code>.\n</p>\n\n<p align=\"center\">\n<a href=\"https://github.com/flowaicom/flow-judge/stargazers/\" target=\"_blank\">\n <img src=\"https://img.shields.io/github/stars/flowaicom/flow-judge?style=social&label=Star&maxAge=3600\" alt=\"GitHub stars\">\n</a>\n<a href=\"https://github.com/flowaicom/flow-judge/releases\" target=\"_blank\">\n <img src=\"https://img.shields.io/github/v/release/flowaicom/flow-judge?color=white\" alt=\"Release\">\n</a>\n<a href=\"https://www.youtube.com/@flowaicom\" target=\"_blank\">\n <img alt=\"YouTube Channel Views\" src=\"https://img.shields.io/youtube/channel/views/UCo2qL1nIQRHiPc0TF9xbqwg?style=social\">\n</a>\n<a href=\"https://github.com/flowaicom/flow-judge/actions/workflows/test-and-lint.yml\" target=\"_blank\">\n <img src=\"https://github.com/flowaicom/flow-judge/actions/workflows/test-and-lint.yml/badge.svg\" alt=\"Build\">\n</a>\n<a href=\"https://codecov.io/gh/flowaicom/flow-judge\" target=\"_blank\">\n <img src=\"https://codecov.io/gh/flowaicom/flow-judge/branch/main/graph/badge.svg?token=AEGC7W3DGE\" alt=\"Code coverage\">\n</a>\n<a href=\"https://github.com/flowaicom/flow-judge/blob/main/LICENSE\" target=\"_blank\">\n <img src=\"https://img.shields.io/static/v1?label=license&message=Apache%202.0&color=white\" alt=\"License\">\n</a>\n<a href=\"https://app.fossa.com/projects/git%2Bgithub.com%2Fflowaicom%2Fflow-judge?ref=badge_shield\" alt=\"FOSSA Status\"><img src=\"https://app.fossa.com/api/projects/git%2Bgithub.com%2Fflowaicom%2Fflow-judge.svg?type=shield\"/></a>\n</p>\n\n\n## Model\n`Flow-Judge-v0.1` is an open, small yet powerful language model evaluator trained on a synthetic dataset containing LLM system evaluation data by Flow AI.\n\nYou can learn more about the unique features of our model in the [technical report](https://www.flow-ai.com/blog/flow-judge#flow-judge-an-open-small-language-model-for-llm-system-evaluations).\n\n\n## Features of the library\n\n- Support for multiple model types: Hugging Face Transformers and vLLM\n- Extensible architecture for custom metrics\n- Pre-defined evaluation metrics\n- Ease of custom metric and rubric creation\n- Batched evaluation for efficient processing\n- Integrations with most popular frameworks like Llama Index\n\n## Installation\n\nInstall flow-judge using pip:\n\n```bash\npip install -e \".[vllm,hf]\"\npip install 'flash_attn>=2.6.3' --no-build-isolation\n```\n\nExtras available:\n- `dev` to install development dependencies\n- `hf` to install Hugging Face Transformers dependencies\n- `vllm` to install vLLM dependencies\n- `llamafile` to install Llamafile dependencies\n- `baseten` to install Baseten dependencies\n\n## Quick Start\n\nHere's a simple example to get you started:\n\n```python\nfrom flow_judge import Vllm, Llamafile, Hf, EvalInput, FlowJudge\nfrom flow_judge.metrics import RESPONSE_FAITHFULNESS_5POINT\nfrom IPython.display import Markdown, display\n\n# If you are running on an Ampere GPU or newer, create a model using VLLM\nmodel = Vllm()\n\n# If you have other applications open taking up VRAM, you can use less VRAM by setting gpu_memory_utilization to a lower value.\n# model = Vllm(gpu_memory_utilization=0.70)\n\n# Or if not running on Ampere GPU or newer, create a model using no flash attn and Hugging Face Transformers\n# model = Hf(flash_attn=False)\n\n# Or create a model using Llamafile if not running an Nvidia GPU & running a Silicon MacOS for example\n# model = Llamafile()\n\n# Initialize the judge\nfaithfulness_judge = FlowJudge(\n metric=RESPONSE_FAITHFULNESS_5POINT,\n model=model\n)\n\n# Sample to evaluate\nquery = \"\"\"...\"\"\"\ncontext = \"\"\"...\"\"\"\"\nresponse = \"\"\"...\"\"\"\n\n# Create an EvalInput\n# We want to evaluate the response to the customer issue based on the context and the user instructions\neval_input = EvalInput(\n inputs=[\n {\"query\": query},\n {\"context\": context},\n ],\n output={\"response\": response},\n)\n\n# Run the evaluation\nresult = faithfulness_judge.evaluate(eval_input, save_results=False)\n\n# Display the result\ndisplay(Markdown(f\"__Feedback:__\\n{result.feedback}\\n\\n__Score:__\\n{result.score}\"))\n```\n\n## Usage\n\n### Inference Options\n\nThe library supports multiple inference backends to accommodate different hardware configurations and performance needs:\n\n1. **vLLM**:\n - Best for NVIDIA GPUs with Ampere architecture or newer (e.g., RTX 3000 series, A100, H100)\n - Offers the highest performance and throughput\n - Requires CUDA-compatible GPU\n\n ```python\n from flow_judge import Vllm\n\n model = Vllm()\n ```\n\n2. **Hugging Face Transformers**:\n - Compatible with a wide range of hardware, including older NVIDIA GPUs\n - Supports CPU inference (slower but universally compatible)\n - It is slower than vLLM but generally compatible with more hardware.\n\n If you are running on an Ampere GPU or newer:\n ```python\n from flow_judge import Hf\n\n model = Hf()\n ```\n\n If you are not running on an Ampere GPU or newer, disable flash attention:\n ```python\n from flow_judge import Hf\n\n model = Hf(flash_attn=False)\n ```\n\n3. **Llamafile**:\n - Ideal for non-NVIDIA hardware, including Apple Silicon\n - Provides good performance on CPUs\n - Self-contained, easy to deploy option\n\n ```python\n from flow_judge import Llamafile\n\n model = Llamafile()\n ```\n\n4. **Baseten**:\n - Remote execution.\n - Machine independent.\n - Improved concurrency patterns for larger workloads.\n\n ```python\n from flow_judge import Baseten\n\n model = Baseten()\n ```\n For detailed information on using Baseten, visit the [Baseten readme](https://github.com/flowaicom/flow-judge/blob/feat/baseten-integration/flow_judge/models/adapters/baseten/README.md).\n\nChoose the inference backend that best matches your hardware and performance requirements. The library provides a unified interface for all these options, making it easy to switch between them as needed.\n\n\n### Evaluation Metrics\n\n`Flow-Judge-v0.1` was trained to handle any custom metric that can be expressed as a combination of evaluation criteria and rubric, and required inputs and outputs.\n\n#### Pre-defined Metrics\n\nFor convenience, `flow-judge` library comes with pre-defined metrics such as `RESPONSE_CORRECTNESS` or `RESPONSE_FAITHFULNESS`. You can check the full list by running:\n\n```python\nfrom flow_judge.metrics import list_all_metrics\n\nlist_all_metrics()\n```\n\n### Batched Evaluations\n\nFor efficient processing of multiple inputs, you can use the `batch_evaluate` method:\n\n```python\n# Read the sample data\nimport json\nfrom flow_judge import Vllm, EvalInput, FlowJudge\nfrom flow_judge.metrics import RESPONSE_FAITHFULNESS_5POINT\nfrom IPython.display import Markdown, display\n\n# Initialize the model\nmodel = Vllm()\n\n# Initialize the judge\nfaithfulness_judge = FlowJudge(\n metric=RESPONSE_FAITHFULNESS_5POINT,\n model=model\n)\n\n# Load some sampledata\nwith open(\"sample_data/csr_assistant.json\", \"r\") as f:\n data = json.load(f)\n\n# Create a list of inputs and outputs\ninputs_batch = [\n [\n {\"query\": sample[\"query\"]},\n {\"context\": sample[\"context\"]},\n ]\n for sample in data\n]\noutputs_batch = [{\"response\": sample[\"response\"]} for sample in data]\n\n# Create a list of EvalInput\neval_inputs_batch = [EvalInput(inputs=inputs, output=output) for inputs, output in zip(inputs_batch, outputs_batch)]\n\n# Run the batch evaluation\nresults = faithfulness_judge.batch_evaluate(eval_inputs_batch, save_results=False)\n\n# Visualizing the results\nfor i, result in enumerate(results):\n display(Markdown(f\"__Sample {i+1}:__\"))\n display(Markdown(f\"__Feedback:__\\n{result.feedback}\\n\\n__Score:__\\n{result.score}\"))\n display(Markdown(\"---\"))\n```\n\n## Advanced Usage\n\n> [!WARNING]\n> There exists currently a reported issue with Phi-3 models that produces gibberish outputs with contexts longer than 4096 tokens, including input and output. This issue has been recently fixed in the transformers library so we recommend using the `Hf()` model configuration for longer contexts at the moment. For more details, refer to: [#33129](https://github.com/huggingface/transformers/pull/33129) and [#6135](https://github.com/vllm-project/vllm/issues/6135)\n\n\n### Custom Metrics\n\nCreate your own evaluation metrics:\n\n```python\nfrom flow_judge.metrics import CustomMetric, RubricItem\n\ncustom_metric = CustomMetric(\n name=\"My Custom Metric\",\n criteria=\"Evaluate based on X, Y, and Z.\",\n rubric=[\n RubricItem(score=0, description=\"Poor performance\"),\n RubricItem(score=1, description=\"Good performance\"),\n ],\n required_inputs=[\"query\"],\n required_output=\"response\"\n)\n\njudge = FlowJudge(metric=custom_metric, config=\"Flow-Judge-v0.1-AWQ\")\n```\n\n### Integrations\n\nWe support an integration with Llama Index evaluation module and Haystack:\n- [Llama Index tutorial](https://github.com/flowaicom/flow-judge/blob/main/examples/4_llama_index_evaluators.ipynb)\n- [Haystack tutorial](https://github.com/flowaicom/flow-judge/blob/main/examples/5_evaluate_haystack_rag_pipeline.ipynb)\n\n> Note that we are currently working on adding more integrations with other frameworks in the near future.\n## Development Setup\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/flowaicom/flow-judge.git\n cd flow-judge\n ```\n\n2. Create a virtual environment:\n ```bash\n virtualenv ./.venv\n ```\n or\n\n ```bash\n python -m venv ./.venv\n ```\n\n3. Activate the virtual environment:\n - On Windows:\n ```bash\n venv\\Scripts\\activate\n ```\n - On macOS and Linux:\n ```bash\n source venv/bin/activate\n ```\n\n4. Install the package in editable mode with development dependencies:\n ```bash\n pip install -e \".[dev]\"\n ```\n or\n ```bash\n pip install -e \".[dev,vllm]\"\n ```\n for vLLM support.\n\n5. Set up pre-commit hooks:\n ```bash\n pre-commit install\n ```\n\n6. Make sure you have trufflehog installed:\n ```bash\n # make trufflehog available in your path\n # macos\n brew install trufflehog\n # linux\n curl -sSfL https://raw.githubusercontent.com/trufflesecurity/trufflehog/main/scripts/install.sh | sh -s -- -b /usr/local/bin\n # nix\n nix profile install nixpkgs#trufflehog\n ```\n\n7. Run pre-commit on all files:\n ```bash\n pre-commit run --all-files\n ```\n\n8. You're now ready to start developing! You can run the main script with:\n ```bash\n python -m flow_judge\n ```\n\nRemember to always activate your virtual environment when working on the project. To deactivate the virtual environment when you're done, simply run:\n```bash\ndeactivate\n```\n\n## Running Tests\n\nTo run the tests for Flow-Judge, follow these steps:\n\n1. Navigate to the root directory of the project in your terminal.\n\n2. Run the tests using pytest:\n ```bash\n pytest tests/\n ```\n\n This will discover and run all the tests in the `tests/` directory.\n\n3. If you want to run a specific test file, you can do so by specifying the file path:\n ```bash\n pytest tests/test_flow_judge.py\n ```\n\n4. For more verbose output, you can use the `-v` flag:\n ```bash\n pytest -v tests/\n ```\n## Contributing\n\nContributions to `flow-judge` are welcome! Please follow these steps:\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\nPlease ensure that your code adheres to the project's coding standards and passes all tests.\n\n## License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n[](https://app.fossa.com/projects/git%2Bgithub.com%2Fflowaicom%2Fflow-judge?ref=badge_large)\n\n## Acknowledgments\n\nFlow-Judge is developed and maintained by the Flow AI team. We appreciate the contributions and feedback from the AI community in making this tool more robust and versatile.\n",
"bugtrack_url": null,
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