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ragas 版本 0.2.14
llm_model="qwen2.5:32b"
llm = OllamaLLM(base_url = "http://xxx/", model=llm_model)
llm = ChatOllama(base_url = "http://xxx/", model=llm_model,max_tokens=160000)
evaluator_llm = LangchainLLMWrapper(llm,run_config)
#测试是否调用成功
response = llm.invoke("讲一个笑话")
print(response)
embed_model="quentinz/bge-large-zh-v1.5:f32"
embeding = OllamaEmbeddings(base_url = "http://xxxx",model=embed_model/)
wrapped_embeddings = LangchainEmbeddingsWrapper(embeding)
测试生成嵌入向量
text = "This is a test sentence."
embed_vector = embeding.embed_query(text)
以上代码测试没有问题
questions = [
"自动化技术与应用专业的入学基本要求？"
]
correct_answers = [
"中等职业学校毕业、普通高级中学毕业或具备同等学力"
]
samples = []
对每个问题进行处理
for question, correct_answer in zip(questions, correct_answers):
res = session.ask(question, stream=True)
存储 AI 给出的最终答案及检索召回的文本片段
final_answer = ""
retrieved_contexts = []
for ans in res:
final_answer = ans.content
if ans.reference is not None:
for ref in ans.reference:
retrieved_contexts.append(ref['content'])
print("final_answer:",final_answer)
print("retrieved_contexts:",retrieved_contexts)
创建 SingleTurnSample 对象
sample = SingleTurnSample(
user_input=question,
retrieved_contexts=retrieved_contexts,
response=final_answer,
reference=correct_answer
)
samples.append(sample)
print("samples")
生成评估数据集
eval_dataset = EvaluationDataset(samples=samples)
print(eval_dataset)
选择评估指标进行评估
from ragas.metrics import LLMContextRecall, Faithfulness, FactualCorrectness, SemanticSimilarity
from ragas import evaluate
metrics = [
LLMContextRecall(llm=evaluator_llm),
FactualCorrectness(llm=evaluator_llm),
Faithfulness(llm=evaluator_llm),
SemanticSimilarity(embeddings=wrapped_embeddings)
]
results = evaluate(dataset=eval_dataset, metrics=metrics)
print(results)
设置显示所有列，打印结果
import pandas as pd
pd.set_option('display.max_columns', None)
print(results.to_pandas())
df = results.to_pandas()
print(df.head())
df.head().to_csv('res.csv', index=False,encoding='utf-8-sig')
Image
评估结果也是NAN 太难受了 排查好几天了 也不知道问题