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LLM之基于llama-index部署本地embedding与GLM-4模型对rag系统进行测评

article 2025/2/22 5:27:14

前言

有时间在写吧

评估指标

llama-Index 内置了有评估工具，今天教大家如何使用

llama-Index 有以下评估指标：

  • Answer Relevcancy

  • Context Relevancy

  • Relevancy

  • Faithfulness

  • Correctness

感兴趣可以去 llama_index.core.evaluation 文件查看

当然llama-Index 还提供了测试数据的生成功能，可以帮助我们轻松地生成评估所需的测试数据，包括评估的问题、参考答案等，这样我们就可以快速地进行评估工作，而不需要花费大量的时间去准备测试数据

生成测试数据

from llama_index.core.llama_dataset.rag import LabelledRagDataset
from llama_index.core.llama_dataset.generator import RagDatasetGenerator
from llama_index.core.evaluation import AnswerRelevancyEvaluator
from llama_index.core.node_parser import SentenceSplitter
import os
from typing import Any

from llama_index.core.llms import (
    CustomLLM,
    CompletionResponse,
    CompletionResponseGen,
    LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from transformers import AutoTokenizer, AutoModelForCausalLM
from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from datetime import datetime
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.evaluation import ContextRelevancyEvaluator

class GLMCustomLLM(CustomLLM):
    context_window: int = 128000  # 上下文窗口大小
    num_output: int = 18000  # 输出的token数量
    model_name: str = "glm-4-9b-chat"  # 模型名称
    tokenizer: object = None  # 分词器
    model: object = None  # 模型
    dummy_response: str = "My response"

    def __init__(self, pretrained_model_name_or_path):
        super().__init__()

        # GPU方式加载模型
        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cuda",
                                                       trust_remote_code=True)
        self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cuda",
                                                          trust_remote_code=True).eval()
        self.model = self.model.float()

    @property
    def metadata(self) -> LLMMetadata:
        """Get LLM metadata."""
        # 得到LLM的元数据
        return LLMMetadata(
            context_window=self.context_window,
            num_output=self.num_output,
            model_name=self.model_name,
        )


    @llm_completion_callback()  # 回调函数
    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        now = datetime.now()
        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        # 完成函数
        # print(f"完成函数 当前时间为{now} response={response}")
        return CompletionResponse(text=response)

    @llm_completion_callback()
    def stream_complete(
            self, prompt: str, **kwargs: Any
    ) -> CompletionResponseGen:
        # 流式完成函数
        now = datetime.now()
        print("流式完成函数")

        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        for token in response:
            yield CompletionResponse(text=token, delta=token)

llm = GLMCustomLLM(pretrained_model_name_or_path='/home/kelvin/nlp/model/LLM/THUDM/glm-4-9b-chat')

embed_model_path = '/home/kelvin/nlp/model/Embedding/BAAI/bge-m3'


#输入文件夹的路径
documents = SimpleDirectoryReader("/home/kelvin/nlp/GLM-4/basic_demo/input").load_data()

dataset_generator = RagDatasetGenerator.from_documents(
    documents,
    llm=llm,
    num_questions_per_chunk=1,
)
dataset = dataset_generator.generate_questions_from_nodes()
examples = dataset.examples
for i, example in enumerate(examples):
    contexts = [n[:100] for n in example.reference_contexts]
    print(f"{i + 1}. {example.query}")

#测试数据保存路径
dataset_json = "/home/kelvin/nlp/GLM-4/basic_demo/test-dataset.json"
# dataset = LabelledRagDataset.from_json(dataset_json)
# examples = dataset.examples

if not os.path.exists(dataset_json):
    dataset = dataset_generator.generate_dataset_from_nodes()
    examples = dataset.examples
    dataset.save_json(dataset_json)
else:
    dataset = LabelledRagDataset.from_json(dataset_json)
    examples = dataset.examples

Answer Relevcancy与Context Relevancy

Answer Revelancy 是评估 Answer 和 Question 的相关性，这个指标可以帮助我们评估生成的答案是否和问题相关
 

Context Relevancy 是评估 Context 和 Question 的相关性，这个指标可以帮助我们评估检索到的文档上下文和问题的相关性

Answer Revelancy：将问题和答案传递给AnswerRelevancyEvaluator评估器，通过evaluate方法来评估问题和答案的相关性

Context Relevancy：将问题和检索到的文档上下文传递给ContextRelevancyEvaluator评估器，通过evaluate方法来评估问题和答案的相关性

评估结果的score范围是 0~1，得分越高表示答案和问题的相关性越高，得分为 1 表示完全相关

评估结果中还有feedback属性，用来解释评估结果，这个属性可以帮助我们了解评估结果的产生原因

示例代码：

from llama_index.core.llama_dataset.rag import LabelledRagDataset
from llama_index.core.llama_dataset.generator import RagDatasetGenerator
from llama_index.core.evaluation import AnswerRelevancyEvaluator
from llama_index.core.node_parser import SentenceSplitter
import os
from typing import Any

from llama_index.core.llms import (
    CustomLLM,
    CompletionResponse,
    CompletionResponseGen,
    LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from transformers import AutoTokenizer, AutoModelForCausalLM
from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from datetime import datetime
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.evaluation import ContextRelevancyEvaluator

class GLMCustomLLM(CustomLLM):
    context_window: int = 128000  # 上下文窗口大小
    num_output: int = 18000  # 输出的token数量
    model_name: str = "glm-4-9b-chat"  # 模型名称
    tokenizer: object = None  # 分词器
    model: object = None  # 模型
    dummy_response: str = "My response"

    def __init__(self, pretrained_model_name_or_path):
        super().__init__()

        # GPU方式加载模型
        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cuda",
                                                       trust_remote_code=True)
        self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cuda",
                                                          trust_remote_code=True).eval()
        self.model = self.model.float()

    @property
    def metadata(self) -> LLMMetadata:
        """Get LLM metadata."""
        # 得到LLM的元数据
        return LLMMetadata(
            context_window=self.context_window,
            num_output=self.num_output,
            model_name=self.model_name,
        )


    @llm_completion_callback()  # 回调函数
    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        now = datetime.now()
        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        # 完成函数
        # print(f"完成函数 当前时间为{now} response={response}")
        return CompletionResponse(text=response)

    @llm_completion_callback()
    def stream_complete(
            self, prompt: str, **kwargs: Any
    ) -> CompletionResponseGen:
        # 流式完成函数
        now = datetime.now()
        print("流式完成函数")

        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        for token in response:
            yield CompletionResponse(text=token, delta=token)

llm = GLMCustomLLM(pretrained_model_name_or_path='/home/kelvin/nlp/model/LLM/THUDM/glm-4-9b-chat')

embed_model_path = '/home/kelvin/nlp/model/Embedding/BAAI/bge-m3'


documents = SimpleDirectoryReader("/home/kelvin/nlp/GLM-4/basic_demo/input").load_data()


dataset_json = "/home/kelvin/nlp/GLM-4/basic_demo/test-dataset.json"
dataset = LabelledRagDataset.from_json(dataset_json)
examples = dataset.examples


question = examples[0].query

node_parser = SentenceSplitter()
nodes = node_parser.get_nodes_from_documents(documents)
Settings.llm = llm
Settings.embed_model = HuggingFaceEmbedding(
    model_name=f"{embed_model_path}", device='cuda'

)
print(f'检索中...')
vector_index = VectorStoreIndex(nodes)
engine = vector_index.as_query_engine()
response = engine.query(question)
answer = str(response)

print(f"question={question}")
print(f'************')
print(f"Answer: {answer}")
evaluator = AnswerRelevancyEvaluator(llm)
result = evaluator.evaluate(query=question, response=answer)
print(f"score: {result.score}")
print(f"feedback: {result.feedback}")

print(f'*****----------******')
contexts = [n.get_content() for n in response.source_nodes]
evaluator = ContextRelevancyEvaluator(llm)
result = evaluator.evaluate(query=question, contexts=contexts)
print(f"ContextRelevancy_score: {result.score}")
print(f"ContextRelevancy_feedback: {result.feedback}")
print(f'ContextRelevancy_query: {result.query}"')

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