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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2023 KubeAGI] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/pypi/ragas_once/README.md b/pypi/ragas_once/README.md index 1001aaddf..c2b0565cb 100644 --- a/pypi/ragas_once/README.md +++ b/pypi/ragas_once/README.md @@ -1,3 +1,60 @@ -# ragas-once +# Ragas CLI -A one-stop cli for ragas(RAG Evaluation) \ No newline at end of file +A one-step Ragas cli tool to evaluate QCAG testsets generated by RAG apps. (Q = Question, C = Contexts, A = Answer, G = Ground_truth) + +## Install with pip + +```bash +pip install ragas langchain==0.0.354 +``` + +then: + +```bash +pip install ragas_once +``` + +## Arguments + +- `--model`: Specifies the model to use for evaluation. + - Default value is "gpt-3.5-turbo". Langchain compatible. +- `--api_base`: Specifies the base URL for the API. + - Default value is "https://api.openai.com/v1". +- `--api_key`: Specifies the API key to authenticate requests. + - Not required if using psuedo-openai API server, e.g. vLLM, Fastchat, etc. +- `--embeddings`: Specifies the Huggingface embeddings model to use for evaluation. + - Embeddings will run **locally**. + - Will use OpenAI embeddings if not set. + - Better set if using psuedo-openai API server. +- `--metrics`: Specifies the metrics to use for evaluation. + - Will use Ragas default metrics if not set. + - Default metrics: `["answer_relevancy", "context_precision", "faithfulness", "context_recall", "context_relevancy"]` + - Other metrics: `"answer_similarity", "answer_correctness"` +- `--dataset`: Specifies the path to the dataset for evaluation. + - Dataset format must meet RAGAS requirements. + - Will use fiqa dataset as demo if not set. + +## Usage + +### Fiqa dataset demo: + +```bash +python3 -m ragas_once.cli --api_key "YOUR_OPENAI_API_KEY" +``` + +### Evaluate with GPT-4 and `BAAI/bge-small-en` embeddings + +The huggingface embeddings will run locally, so **Make sure your machine works and have [sentence-transformers](https://pypi.org/project/sentence-transformers/) installed:** + +```bash +pip install sentence-transformers +``` +Then run: + +```bash +python3 -m ragas_once.cli --model "gpt-4" --api_key "YOUR_OPENAI_API_KEY" --embeddings "BAAI/bge-small-en" --dataset "path/to/dataset.csv" +``` + +### Prepare Dataset + +See [**Ragas documentation**](https://docs.ragas.io/en/stable/howtos/applications/data_preparation.html) \ No newline at end of file diff --git a/pypi/ragas_once/pyproject.toml b/pypi/ragas_once/pyproject.toml new file mode 100644 index 000000000..c80b42e96 --- /dev/null +++ b/pypi/ragas_once/pyproject.toml @@ -0,0 +1,27 @@ +[build-system] +requires = [ + "setuptools>=61.0" + "ragas" + "langchain==0.0.354" +] +build-backend = "setuptools.build_meta" + +[tool.poetry] + +[project] +name = "ragas_once" +version = "0.0.1" +authors = [ + { name = "Kielo", email = "lanture1064@gmail.com" }, +] +description = "A one-step Ragas cli tool to evaluate RAG apps" +readme = "README.md" +requires-python = ">=3.7" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +[tool.poetry.entry-points] +ro = "ragas_once.cli:main" \ No newline at end of file diff --git a/pypi/ragas_once/ragas_once/__init__.py b/pypi/ragas_once/ragas_once/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pypi/ragas_once/ragas_once/cli.py b/pypi/ragas_once/ragas_once/cli.py new file mode 100644 index 000000000..35d689200 --- /dev/null +++ b/pypi/ragas_once/ragas_once/cli.py @@ -0,0 +1,68 @@ +import argparse +import pandas as pd +import ragas_once.wrapper as pkg +from ragas import evaluate +from datasets import Dataset +from datasets import load_dataset + +def main(): + """ + This function is the entry point for the program. It parses command line arguments and sets up the necessary variables for evaluation. + + Parameters: + None + + Returns: + The result of evaluating the test set using the specified metrics. + """ + parser = argparse.ArgumentParser(description='RAGAS CLI') + parser.add_argument("--model", type=str, default="gpt-3.5-turbo", + help="Specifies the model to use for evaluation. Defaults to gpt-3.5-turbo.") + parser.add_argument("--apibase", type=str, default="https://api.openai.com/v1", + help="Specifies the base URL for the API. Defaults to OpenAI.") + parser.add_argument("--apikey", type=str, + help="Specifies the API key to authenticate requests.") + parser.add_argument("--embeddings", type=str, + help="Specifies Huggingface embeddings model (or its path) to use for evaluation. Will use OpenAI embeddings if not set.") + parser.add_argument("--metrics", type=list, default=[], + help="Specifies the metrics to use for evaluation.") + parser.add_argument("--dataset", type=str, + help="Specifies the path to the dataset for evaluation. Will use fiqa dataset if not set.") + + args = parser.parse_args() + + model = args.model + api_base = args.apibase + api_key = args.apikey + metrics = args.metrics + dataset = args.dataset + + judge_model = pkg.wrap_langchain_llm(model, api_base, api_key) + + embeddings_model_name = args.embeddings + + if embeddings_model_name: + embeddings = pkg.wrap_embeddings('huggingface', embeddings_model_name, None) + else: + embeddings = pkg.wrap_embeddings('openai', None, api_key) + + if dataset: + data = pd.read_csv(dataset) + data['ground_truths'] = data['ground_truths'].apply(lambda x: x.split(';')) + data['contexts'] = data['contexts'].apply(lambda x: x.split(';')) + test_set = Dataset.from_pandas(data) + else: + print('test_set not provided, using fiqa dataset') + fiqa = load_dataset('explodinggradients/fiqa', 'ragas_eval') + test_set = fiqa["baseline"].select(range(5)) + + ms = pkg.set_metrics(metrics, judge_model, embeddings) + + result = evaluate(test_set, ms) + print(result) + + result.to_pandas().to_csv('result.csv') + +if __name__ == '__main__': + main() + diff --git a/pypi/ragas_once/ragas_once/wrapper.py b/pypi/ragas_once/ragas_once/wrapper.py new file mode 100644 index 000000000..642c5b266 --- /dev/null +++ b/pypi/ragas_once/ragas_once/wrapper.py @@ -0,0 +1,126 @@ +import os +from datasets import Dataset +from langchain.chat_models import ChatOpenAI +from ragas.llms import RagasLLM +from ragas.llms import LangchainLLM +from ragas.embeddings import RagasEmbeddings +from ragas.embeddings import OpenAIEmbeddings +from ragas.embeddings import HuggingfaceEmbeddings +from ragas.metrics.base import Metric + +from ragas.metrics import ( + context_precision, + context_recall, + context_relevancy, + answer_relevancy, + answer_correctness, + answer_similarity, + faithfulness +) + +DEFAULT_METRICS = [ + "answer_relevancy", + "context_precision", + "faithfulness", + "context_recall", + "context_relevancy" + ] + +def wrap_langchain_llm( + model: str, + api_base: str | None, + api_key: str | None +) -> LangchainLLM: + """ + Initializes and returns an instance of the LangchainLLM class. + + Args: + model (str): The name of the language model to use. + api_base (str | None): The base URL for the OpenAI API. If None, the default URL is assumed. + api_key (str | None): The API key for the OpenAI API. + + Returns: + LangchainLLM: An instance of the LangchainLLM class. + + Raises: + ValueError: If api_key is not provided. + + Notes: + - If api_base is not provided, the default URL 'https://api.openai.com/v1' is assumed. + - The environment variables OPENAI_API_KEY and OPENAI_API_BASE are set to the provided api_key and api_base. + """ + if api_base is None: + print('api_base not provided, assuming OpenAI default') + if api_key is None: + raise ValueError("api_key must be provided") + os.environ['OPENAI_API_KEY'] = api_key + base = ChatOpenAI(model_name=model) + else: + os.environ['OPENAI_API_BASE'] = api_base + if api_key: + os.environ['OPENAI_API_KEY'] = api_key + base = ChatOpenAI( + model_name=model, + openai_api_key=api_key, + openai_api_base=api_base + ) + return LangchainLLM(llm=base) + + +def set_metrics( + metrics: list[str], + llm: RagasLLM | None, + embeddings: RagasEmbeddings | None +) -> list[Metric]: + """ + Sets the metrics for evaluation. + + Parameters: + metrics (list[str]): A list of metric names to be set. + llm (RagasLLM | None): An instance of RagasLLM or None. If not set, the code will use OpenAI ChatGPT as default. + embeddings (RagasEmbeddings | None): An instance of RagasEmbeddings or None. If not set, the code will use OpenAI Embeddings as default. + + Returns: + list[Metric]: A list of Metric objects representing the set metrics. + """ + ms = [] + if llm: + context_precision.llm = llm + context_recall.llm = llm + context_relevancy.llm = llm + answer_correctness.llm = llm + answer_similarity.llm = llm + faithfulness.llm = llm + if embeddings: + answer_relevancy.embeddings = embeddings + answer_correctness.embeddings = embeddings + if not metrics: + metrics = DEFAULT_METRICS + for m in metrics: + if m == 'context_precision': + ms.append(context_precision) + elif m == 'context_recall': + ms.append(context_recall) + elif m == 'context_relevancy': + ms.append(context_relevancy) + elif m == 'answer_relevancy': + ms.append(answer_relevancy) + elif m == 'answer_correctness': + ms.append(answer_correctness) + elif m == 'answer_similarity': + ms.append(answer_similarity) + elif m == 'faithfulness': + ms.append(faithfulness) + return ms + +def wrap_embeddings( + model_type: str, + model_name: str | None, + api_key: str | None +) -> RagasEmbeddings: + if model_type == 'openai': + return OpenAIEmbeddings(api_key=api_key) + elif model_type == 'huggingface': + return HuggingfaceEmbeddings(model_name=model_name) + else: + raise ValueError(f"Invalid model type: {model_type}") \ No newline at end of file diff --git a/pypi/ragas_once/setup.py b/pypi/ragas_once/setup.py new file mode 100644 index 000000000..a448973c6 --- /dev/null +++ b/pypi/ragas_once/setup.py @@ -0,0 +1,33 @@ +# make a setup.py for ragacli package + +from setuptools import setup, find_packages + +with open("README.md", "r", encoding="utf-8") as f: + long_description = f.read() + +setup( + name="ragas_once", + version="0.0.1", + author="Kielo", + author_email="lanture1064@gmail.com", + description="A one-step cli tool for RAGAS", + url="https://github.com/kubeagi/arcadia/pypi/ragas_once", + long_description=long_description, + long_description_content_type="text/markdown", + packages=find_packages(), + classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + ], + python_requires=">=3.8", + install_requires=[ + 'ragas', + 'langchain==0.0.354', + ], + entry_points={ + 'console_scripts': [ + 'ro = ragas_once.cli:main' + ] + } +) \ No newline at end of file