From 6e4557d144a895728de2f4a50b507456b93c7429 Mon Sep 17 00:00:00 2001 From: HuXiangkun Date: Wed, 18 Sep 2024 05:57:39 +0000 Subject: [PATCH] customized function for llm invoking and tutorial --- README.md | 12 +- pyproject.toml | 4 +- ragchecker/evaluator.py | 16 +- tutorial/figures/claim_checking.png | Bin 0 -> 123524 bytes tutorial/figures/generator_metrics.png | Bin 0 -> 164839 bytes tutorial/figures/overall_metrics.png | Bin 0 -> 63134 bytes tutorial/figures/rag_pipeline.png | Bin 0 -> 59363 bytes tutorial/figures/retriever_metrics.png | Bin 0 -> 71993 bytes tutorial/ragchecker_tutorial_en.md | 377 +++++++++++++++++++++++++ tutorial/ragchecker_tutorial_zh.md | 372 ++++++++++++++++++++++++ 10 files changed, 763 insertions(+), 18 deletions(-) create mode 100644 tutorial/figures/claim_checking.png create mode 100644 tutorial/figures/generator_metrics.png create mode 100644 tutorial/figures/overall_metrics.png create mode 100644 tutorial/figures/rag_pipeline.png create mode 100644 tutorial/figures/retriever_metrics.png create mode 100644 tutorial/ragchecker_tutorial_en.md create mode 100644 tutorial/ragchecker_tutorial_zh.md diff --git a/README.md b/README.md index 174b4e3..90f476d 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,9 @@ # RAGChecker: A Fine-grained Framework For Diagnosing RAG +

+RAGChecker Paper    |    Tutorial (English)    |    中文教程 +

+ RAGChecker is an advanced automatic evaluation framework designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It provides a comprehensive suite of metrics and tools for in-depth analysis of RAG performance.

@@ -86,8 +90,8 @@ If you are using AWS Bedrock version of Llama3 70B for the claim extractor and c ragchecker-cli \ --input_path=examples/checking_inputs.json \ --output_path=examples/checking_outputs.json \ - --extractor_name=bedrock/meta.llama3-70b-instruct-v1:0 \ - --checker_name=bedrock/meta.llama3-70b-instruct-v1:0 \ + --extractor_name=bedrock/meta.llama3-1-70b-instruct-v1:0 \ + --checker_name=bedrock/meta.llama3-1-70b-instruct-v1:0 \ --batch_size_extractor=64 \ --batch_size_checker=64 \ --metrics all_metrics \ @@ -133,8 +137,8 @@ with open("examples/checking_inputs.json") as fp: # set-up the evaluator evaluator = RAGChecker( - extractor_name="bedrock/meta.llama3-70b-instruct-v1:0", - checker_name="bedrock/meta.llama3-70b-instruct-v1:0", + extractor_name="bedrock/meta.llama3-1-70b-instruct-v1:0", + checker_name="bedrock/meta.llama3-1-70b-instruct-v1:0", batch_size_extractor=32, batch_size_checker=32 ) diff --git a/pyproject.toml b/pyproject.toml index 5e4b98d..9d19122 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "ragchecker" -version = "0.1.6" +version = "0.1.7" description = "RAGChecker: A Fine-grained Framework For Diagnosing Retrieval-Augmented Generation (RAG) systems." authors = [ "Xiangkun Hu ", @@ -15,7 +15,7 @@ license = "Apache-2.0" [tool.poetry.dependencies] python = "^3.9" -refchecker = "^0.2.6" +refchecker = "^0.2.10" loguru = "^0.7" dataclasses-json = "^0.6" diff --git a/ragchecker/evaluator.py b/ragchecker/evaluator.py index f5e04bb..4cb28ca 100644 --- a/ragchecker/evaluator.py +++ b/ragchecker/evaluator.py @@ -51,9 +51,7 @@ def __init__( openai_api_key=None, joint_check=True, joint_check_num=5, - sagemaker_client=None, - sagemaker_params=None, - sagemaker_get_response_func=None, + custom_llm_api_func=None, **kwargs ): if openai_api_key: @@ -62,9 +60,7 @@ def __init__( self.joint_check = joint_check self.joint_check_num = joint_check_num self.kwargs = kwargs - self.sagemaker_client = sagemaker_client - self.sagemaker_params = sagemaker_params - self.sagemaker_get_response_func = sagemaker_get_response_func + self.custom_llm_api_func = custom_llm_api_func self.extractor = LLMExtractor( model=extractor_name, @@ -111,9 +107,7 @@ def extract_claims(self, results: List[RAGResult], extract_type="gt_answer"): batch_responses=texts, batch_questions=questions, max_new_tokens=self.extractor_max_new_tokens, - sagemaker_client=self.sagemaker_client, - sagemaker_params=self.sagemaker_params, - sagemaker_get_response_func=self.sagemaker_get_response_func, + custom_llm_api_func=self.custom_llm_api_func, **self.kwargs ) claims = [[c.content for c in res.claims] for res in extraction_results] @@ -174,9 +168,7 @@ def check_claims(self, results: RAGResults, check_type="answer2response"): merge_psg=merge_psg, is_joint=self.joint_check, joint_check_num=self.joint_check_num, - sagemaker_client=self.sagemaker_client, - sagemaker_params=self.sagemaker_params, - sagemaker_get_response_func=self.sagemaker_get_response_func, + custom_llm_api_func=self.custom_llm_api_func, **self.kwargs ) for i, result in enumerate(results): diff --git a/tutorial/figures/claim_checking.png b/tutorial/figures/claim_checking.png new file mode 100644 index 0000000000000000000000000000000000000000..4d50ec8cebef507b69d537b8531bb140a12aa6a5 GIT binary patch literal 123524 zcmeGEWmJ^y`acc}($d}CAu@C$jdUp`B_-X`AxPJd(kYUX(kTc7($XNINOw2?lfC!7 zZ};!J*M9Z9eAa@+P;*^##&LY=oNzT2ISe#XvZ1>>7!zdJZ@RMcr zb^-7O&P78`^1-Je@(u8fw7HIgg|hMkR`3|*0bJC-~PKxCalxSGfD1 z<-q^-TV%VOhkreO7jw?nE?P>-A z0lJL}vD!eX>4O2(uCyBFRq`iWB@K^@vQ1T(83!T)wFls8Xy3xlBz7oc-5eGhDGEPY zuI^;FA87s1To$XhEb_Y=FDCq6sGP3AiGvOIzdo2Ha?At0v?Bvh!v4ochb#OT^hE4` zKNXCYnnEx#=2Q6pc%lFldAUh%2?Xi?@j)FK;JU&t)Wir+OZ`7SD0KJ!=PSbwWCu50 zv%a48KVMpat63}Z|GFIsglcfpLwWf&|MPb42Z-|jKS2LujQ)Q$Km)W0|Me;uGRFH$ zoeu3c?p66`yFa|ENGkG3t=VH0(M2&@`9-3~7Rcyiqlm?P8uplC2Xz|k8h3Wv3R21dauNd=g77{yQ6QH1Z;oPBoe!VBJ&30X@RY 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z@PSRQZhh2n7Ifh!XRL9ptwz@90ah)o2_lNP1mPjt_ji#cR3bP*DE9&d3O=`b2>N>V*`f6NGNH z+g0VNJ>m6qDRA|;$_x8pJwPQ=l|!EwRyoS*{#B69#u%!Q!>2SG3Uq6Av>EJ8k!1#} zRC8Q@_BNK3jB%$Q4LKp6gxNipos^VU4f2%8-1rnx(pw4*+4eRrEvePs4RQV%pX^At zJwMUVix8rdxzA*E=+hNOARoK!L;#eFS;;}g)WE)~y8=zBiO#g=cGWJv7 zMsI-$61i|eGHMZV1NYcII93S4!^4+{UU2muYSQnCl1Oc&ZWiIQ9Tx9G*jPe9Q5 zSK#*%^;uE^(EYvZ!DQdLiNgNfv!)QPdcT_mKcTV6j7h>tG`99Dpne;9(>&z=1FKM>w*UYD literal 0 HcmV?d00001 diff --git a/tutorial/ragchecker_tutorial_en.md b/tutorial/ragchecker_tutorial_en.md new file mode 100644 index 0000000..c1020ed --- /dev/null +++ b/tutorial/ragchecker_tutorial_en.md @@ -0,0 +1,377 @@ +# RAGChecker Tutorial + +Table of Contents: + +- [Introduction to RAGChecker](#introduction-to-ragchecker) +- [Prepare the Benchmark Data and RAG Outputs](#prepare-the-benchmark-data-and-rag-outputs) + - [Benchmark Dataset](#benchmark-dataset) + - [Outputs from RAG](#outputs-from-rag) +- [How RAGChecker Works](#how-ragchecker-works) +- [RAGChecker Metrics](#ragchecker-metrics) + - [Overall Metrics](#overall-metrics) + - [Retriever Metrics](#retriever-metrics) + - [Generator Metrics](#generator-metrics) + - [Summary of RAGChecker Metrics](#summary-of-ragchecker-metrics) +- [How to Use RAGChecker](#how-to-use-ragchecker) + - [Command Line Method](#command-line-method) + - [Embedding in Python Code](#embedding-in-python-code) +- [Interpreting Results and Improving Your RAG System](#interpreting-results-and-improving-your-rag-system) +- [FAQ](#faq) + +## Introduction to RAGChecker + + +RAGChecker is a comprehensive evaluation framework designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. RAG systems combine a retriever, which fetches relevant information from a knowledge base, and a generator, which produces responses based on the retrieved information and the input query. + +RAGChecker helps developers: + +- Identify specific areas of improvement in their RAG systems. +- Understand the interplay between retrieval and generation components. +- Improve their systems for better performance and reliability. + +## Prepare the Benchmark Data and RAG Outputs + +

+ RAG Pipeline +
+ Figure: RAG Pipeline +

+ +#### Benchmark Dataset + +To use RAGChecker, you need to prepare a `benchmark dataset` or test set, consisting of question-answer (QA) pairs: +- **Question**: The real-world question or query that a user might input to the RAG system. +- **Ground Truth Answer (GT Answer)**: The ideal answer to the question, typically written by humans based on available documents. + +Normally, the questions are collected from real-world user cases. Put the QA pairs into a `JSON` file with the following format: + +```json +{ + "results": [ # A list of QA pairs + { + "query_id": "000", + "query": "This is the question for the first example", + "gt_answer": "This is the ground truth answer for the first example" + }, + { + "query_id": "001", + "query": "This is the question for the second example", + "gt_answer": "This is the ground truth answer for the second example" + }, + ... + ] +} +``` + + +#### Outputs from RAG + +For each question in your benchmark, run your RAG system and collect: +- **Retrieved Context**: A list of text chunks retrieved from the knowledge base for the given question. +- **Response**: The final output generated by your RAG system based on the question and retrieved context. + +Add these outputs to your JSON file: + +```json +{ + "results": [ + { + "query_id": "000", + "query": "This is the question for the first example", + "gt_answer": "This is the ground truth answer for the first example", + "response": "This is the RAG response for the first example", + "retrieved_context": [ # A list of chunks returned from the retriever and used for response generation + { + "doc_id": "xxx", + "text": "Content of the first chunk" + }, + { + "doc_id": "xxx", + "text": "Content of the second chunk" + }, + ... + ] + }, + { + "query_id": "001", + "query": "This is the question for the second example", + "gt_answer": "This is the ground truth answer for the second example", + "response": "This is the RAG response for the second example", + "retrieved_context": [ + { + "doc_id": "xxx", + "text": "Content of the first chunk" + }, + { + "doc_id": "xxx", + "text": "Content of the second chunk" + }, + ... + ] + } + ], + ... +} +``` + + +## How RAGChecker Works + +RAGChecker uses a `claim-level checking` approach for fine-grained evaluation. Here's how it works: + +1. **Claim Extraction**:RAGChecker uses a large language model (LLM) as an extractor to break down complex texts (RAG system responses and ground truth answers) into individual claims. A claim is a standalone, atomic piece of information that can be verified as true or false. +Example: +Text: "The Eiffel Tower, built in 1889, is 324 meters tall." +Extracted claims: +``` +("Eiffel Tower", "built in", "1889") +("Eiffel Tower", "height", "324 meters") +``` + +2. **Claim Checking**: Another LLM acts as a checker to verify the accuracy of each extracted claim against reference texts (either the retrieved context or the ground truth answer). + +

+ Claim-level Checking +
+ Figure: Claim-level Checking +

+ +RAGChecker performs the following comparisons: + +- Response claims vs. GT Answer: Measures correctness of the response +- GT Answer claims vs. Response: Measures completeness of the response +- Response claims vs. Retrieved Context: Measures faithfulness and identifies hallucinations +- GT Answer claims vs. Retrieved Context: Evaluates the quality of retrieved information + +These comparisons form the basis for RAGChecker's comprehensive metrics. + + +## RAGChecker Metrics + +RAGChecker provides three categories of metrics to evaluate different aspects of a RAG system: + +#### Overall Metrics + +

+ Overall Metrics +
+ Figure: Overall Metrics +

+ +These metrics measure the end-to-end quality of the entire RAG pipeline: + +- **Precision**: Indicates the `correctness` of the response(proportion of correct claims in the response). +- **Recall**: Indicates the `completeness` of the response(proportion of ground truth claims mentioned in the response). +- **F1 Score**: The harmonic mean of precision and recall, providing an overall quality score. + +The computation of the overall metrics requires `response` and `GT answer`, no need for `retrieved context`. + +### Retriever Metrics + +

+ Retriever Metrics +
+ Figure: Retriever Metrics +

+ +The retriever metrics measure the retriever's (e.g. BM25) ability to find relevant information and reduce noise. + +- **Claim Recall**: Proportion of ground truth claims covered by retrieved chunks. +- **Context Precision**: Proportion of relevant chunks. The irrelevant chunks can be considered as noise. + +> Relevant chunk is the one that contains any of the claims in GT answer (green squares in the figure). + +The computation of retriever metrics requires `GT answer` and `retrieved context`. + +### Generator Metrics + +

+ Generator Metrics +
+ Figure: Generator Metrics +

+ +These metrics evaluate various aspects of the generator's performance: + +- **Context Utilization**: How effectively the generator uses relevant information from the context. +- **Noise Sensitivity in Relevant/Irrelevant Chunks**: How much the generator is influenced by noise in relevant and irrelevant chunks. +- **Hallucination**: Incorrect information generated that's not in the context. +- **Self-knowledge**: Use of the model's own knowledge instead of the context. +- **Faithfulness**: How well the generator sticks to the retrieved context. + + + +### Summary of RAGChecker Metrics + +| Module | Metric | Description | Note | +| -- | -- | -- | -- | +| Overall | Precision | Correctness of the response | | +| | Recall | Completeness of the response | | +| | F1 | Overall quality of the response | | +| Retriever | Claim Recall | Claim-level recall by the retrieved context| | +| | Context Precision | Portion of relevant chunks in retrieved context | | +| Generator | Context Utilization | How effectively the generator uses relevant information in the context | | +| | Noise Sensitivity in Relevant | How much the generator influenced by noise in `relevant chunks` | The lower the better | +| | Noise Sensitivity in Irrelevant | How much the generator influenced by noise in `irrelevant chunks` | The lower the better | +| | Hallucination | Incorrect information made up by the generator | The lower the better | +| | Self-knowledge | Use of the model's own knowledge instead of the context | Whether it is lower the better depends on user's preference | +| | Faithfulness | How well the generator sticks to the retrieved context | | + + + + +## How to Use RAGChecker + +First install dependencies: + +```bash +pip install ragchecker +python -m spacy download en_core_web_sm +``` + + +### Command Line Method + +1. Prepare your input JSON file with the required format (see descriptions above). + +2. Run the evaluation script (using example file in our repo): +```bash +ragchecker-cli \ + --input_path=examples/checking_inputs.json \ # This is your input data with required format + --output_path=examples/checking_outputs.json \ # You will specify an output file here + --extractor_name=bedrock/meta.llama3-1-70b-instruct-v1:0 \ # See description below on how to setup this + --checker_name=bedrock/meta.llama3-1-70b-instruct-v1:0 \ # See description below on how to setup this + --batch_size_extractor=64 \ + --batch_size_checker=128 \ # Can be larger than batch_size_extractor + --metrics all_metrics # all_metrics, overall_metrics, retriever_metrics or generator_metrics +``` + +> 💡 **Notes on setting up LLM for extractor and checker** +> +> RAGChecker employ [litellm](https://docs.litellm.ai/) for invoking the LLMs. Please refer to their documentation for different LLM providers(e.g. OpenAI, AWS Bedrock, AWS Sagemaker, or deployed by vllm): [Supported Model & Providers](https://docs.litellm.ai/docs/providers). +> +> You can also refer to [RefChecker's guidance](https://github.com/amazon-science/RefChecker/tree/main?tab=readme-ov-file#choose-models-for-the-extractor-and-checker) for setting up the LLMs. + +### Embedding in Python Code + +```python +from ragchecker import RAGResults, RAGChecker +from ragchecker.metrics import all_metrics + + +# initialize ragresults from json/dict +with open("examples/checking_inputs.json") as fp: + rag_results = RAGResults.from_json(fp.read()) + +# set-up the evaluator +evaluator = RAGChecker( + extractor_name="bedrock/meta.llama3-1-70b-instruct-v1:0", + checker_name="bedrock/meta.llama3-1-70b-instruct-v1:0", + batch_size_extractor=32, + batch_size_checker=32 +) + +# evaluate results with selected metrics or certain groups, e.g., retriever_metrics, generator_metrics, all_metrics +evaluator.evaluate(rag_results, all_metrics) +print(rag_results) +``` + +> 💡 **Use Your Own LLMs for Extraction and Checking** +> +> As described above, we use litellm for invoking LLMs for claim extraction and checking. But, sometimes we want to use LLM deployed by our own which is not compatiable with litellm, we can use parameter `custom_llm_api_func` to setup the function for invoking our LLM. +> +>```python +># define the function for invoking your own LLM +>def my_llm_api_func(prompts): +> """ +> Get responses from LLM for the input prompts +> Parameters +> ---------- +> prompts: List[str] +> A list of prompts. +> +> Returns +> ---------- +> response_list : List[str] +> A list of generated text. +> """ +> # my code here for invoking the LLM +> # put the responses in `response_list` +> # note: to accelerate the evaluation, please use batching for concurrently get responses for the prompts +> +> return response_list +> +> +># initialize evaluator with customized API function +>evaluator = RAGChecker( +> custom_llm_api_func=my_llm_api_func +>) +>``` +> +>Note that, RAGChecker will invoke many requests, so you should implement `batch invoking` in your function. If your tool cannot directly support batch invoking, please consider using [asyncio](https://docs.python.org/3/library/asyncio.html) for concurrency. + + +Once the code runs successfully, it will output the values for the metrics like follows: + +```json +Results for examples/checking_outputs.json: +{ + "overall_metrics": { + "precision": 73.3, + "recall": 62.5, + "f1": 67.3 + }, + "retriever_metrics": { + "claim_recall": 61.4, + "context_precision": 87.5 + }, + "generator_metrics": { + "context_utilization": 87.5, + "noise_sensitivity_in_relevant": 22.5, + "noise_sensitivity_in_irrelevant": 0.0, + "hallucination": 4.2, + "self_knowledge": 25.0, + "faithfulness": 70.8 + } +} +``` + + +## Interpreting Results and Improving Your RAG System + +After running RAGChecker, you'll receive a set of metrics. Here's how to interpret and act on these results: + +1. **Retriever**: + - Low claim recall: Consider using a more advanced retrieval model or increasing the number of retrieved chunks. + - Low context precision: Try reducing the number of retrieved chunks to reduce noise. + +You can also consider fine-tune your retriever if possible. + +2. **Generator Improvements**: + - Low faithfulness or high hallucination: Adjust your prompts to emphasize using only retrieved information. + - Low context utilization: Modify prompts to encourage the generator to identify and use relevant information. + - High noise sensitivity: Implement reasoning steps in your prompts to help the generator ignore irrelevant information. + +3. **Overall System Improvements**: + - Experiment with different chunk sizes and numbers of retrieved chunks. + - Try different combinations of retrievers and generators. + - If possible, fine-tune both components on domain-specific data. + +## FAQ + +**Q: Can RAGChecker be used for non-English languages?** + +A: While designed for English, using a capable multilingual LLM for extraction and checking can work for other languages. + +**Q: Is RAGChecker suitable for production monitoring?** + +A: For production monitoring, use the reference-free metric of `faithfulness` as it doesn't require ground truth answers. + +**Q: Does RAGChecker support customized metrics?** + +A: Currently, RAGChecker doesn't support custom metrics. Consider forking the project to add your own metrics or use standalone tools for additional evaluation. \ No newline at end of file diff --git a/tutorial/ragchecker_tutorial_zh.md b/tutorial/ragchecker_tutorial_zh.md new file mode 100644 index 0000000..0a4867b --- /dev/null +++ b/tutorial/ragchecker_tutorial_zh.md @@ -0,0 +1,372 @@ +# RAGChecker教程 + +目录: + +- [RAGChecke简介](#ragchecke简介) +- [准备测试数据和RAG输出](#准备测试数据和rag输出) + - [测试数据](#测试数据) + - [获取RAG的输出](#获取rag的输出) +- [RAGChecker的工作原理](#ragchecker的工作原理) +- [RAGChecker的指标](#ragchecker的指标) + - [整体指标](#整体指标) + - [检索指标](#检索指标) + - [生成指标](#生成指标) + - [RAGChecker指标总结](#ragchecker指标总结) +- [如何使用RAGChecker](#如何使用ragchecker) + - [命令行运行RAGChecker](#命令行运行ragchecker) + - [在Python代码中使用RAGChecker](#在python代码中使用ragchecker) +- [解读结果并改进您的RAG系统](#解读结果并改进您的rag系统) +- [FAQ](#faq) + + +## RAGChecke简介 + +RAGChecker是一个全面的评估框架,旨在评估和诊断检索增强生成(RAG)系统。RAG系统结合了信息检索(retriever,从知识库中获取相关信息)和回复生成(generator, 基于检索的信息和输入查询生成回复)。 + +RAGChecker帮助开发人员: + +- 识别RAG系统中需要改进的地方。 +- 理解检索和生成模块之间的相互作用。 +- 改进系统以获得更好的性能。 + +## 准备测试数据和RAG输出 + +

+ RAG Pipeline +
+ Figure: RAG系统的流程 +

+ +#### 测试数据 + +要使用RAGChecker,您需要准备一个基准数据集或测试集,由问答(QA)对组成: +- **问题(Question)**: 用户输入RAG系统的真实问题或查询。 +- **标准答案(Ground Truth Answer)**: 问题的理想答案,通常由人类基于文档编写。 + +通常,问题来自真实用户案例。将问题答案对放入具有以下格式的JSON文件中: + +```json +{ + "results": [ # A list of QA pairs + { + "query_id": "000", + "query": "这是第一个例子的问题", + "gt_answer": "这是第一个例子的标准答案" + }, + { + "query_id": "001", + "query": "这是第二个例子的问题", + "gt_answer": "这是第二个例子的标准答案" + }, + ... + ] +} +``` + + +#### 获取RAG的输出 + +对于测试集中的每个问题,运行您的RAG系统并收集: +- **检索上下文(Retrieved Context)**: 从知识库中检索到的信息。 +- **回复(Response)**: RAG系统基于问题和检索上下文生成的最终输出。 + +将这些输出添加到您的JSON文件中: + +```json +{ + "results": [ + { + "query_id": "000", + "query": "这是第一个例子的问题", + "gt_answer": "这是第一个例子的标准答案", + "response": "这是第一个例子的RAG回复", + "retrieved_context": [ # 检索模块返回的chunk列表 + { + "doc_id": "xxx", + "text": "第一个chunk的内容" + }, + { + "doc_id": "xxx", + "text": "第二个chunk的内容" + }, + ... + ] + }, + { + "query_id": "001", + "query": "这是第二个例子的问题", + "gt_answer": "这是第二个例子的标准答案", + "response": "这是第二个例子的RAG回复", + "retrieved_context": [ + { + "doc_id": "xxx", + "text": "第一个chunk的内容" + }, + { + "doc_id": "xxx", + "text": "第二个chunk的内容" + }, + ... + ] + } + ], + ... +} +``` + + +## RAGChecker的工作原理 + +RAGChecker使用`声明级检查(claim-level checking)`方法进行细粒度评估。以下是其工作原理: + +1. **声明抽取(Claim Extraction)**:RAGChecker使用大型语言模型(LLM)作为抽取器,将复杂文本(RAG系统响应和标准答案)分解为单独的声明。声明是一个独立的、原子化的信息片段,可以被验证为真或假。示例:文本:"埃菲尔铁塔建于1889年,高324米。" +抽取的声明: +``` +("埃菲尔铁塔", "建于", "1889年") +("埃菲尔铁塔", "高度", "324米") +``` + +2. **声明检查(Claim Checking)**: 另一个LLM作为检查器,根据参考文本(检索的上下文或标准答案)验证每个提取的声明的准确性。 + +

+ Claim-level Checking +
+ Figure: 声明级检查 +

+ +RAGChecker执行以下比较: + +- 回复的声明 vs. 标准答案:衡量回复的正确性。 +- 标准答案的声明 vs. 回复:衡量回复的完整性。 +- 回复的声明 vs. 检索上下文:衡量忠实度并识别幻觉。 +- 标准答案的声明 vs. 检索上下文:评估检索信息的质量。 + +这些比较是计算RAGChecker中各种指标的基础。 + + +## RAGChecker的指标 + +RAGChecker提供三类指标来评估RAG系统的不同方面: + +#### 整体指标 + +

+ Overall Metrics +
+ Figure: 整体指标 +

+ +这些指标衡量整个RAG流程的整体质量: + +- **精度(Precision)**: 表示回复的`正确性(correctness)`(回复中正确声明的比例)。 +- **召回(Recall)**: 表示回复的`完整性(completeness)`(回复中提到的标准答案声明的比例)。 +- **F1**: 精确率和召回率的调和平均值,衡量回复的整体质量。 + +计算整体指标需要回复和标准答案,不需要检索上下文。 + +### 检索指标 + +

+ Retriever Metrics +
+ Figure: 检索指标 +

+ +检索指标衡量检索模块(如BM25)搜索相关信息和并降低噪音的能力。 + +- **声明召回(Claim Recall)**:检索上下文覆盖的标准答案声明的比例。 +- **上下文精度(Context Precision)**:相关chunk的比例。不相关的chunk可以被视为噪音。 + +>相关chunk是指包含任意标准答案声明的chunk(图中绿色方块)。 + +计算检索器指标需要标准答案和检索上下文。 + +### 生成指标 + +

+ Generator Metrics +
+ Figure: 生成指标 +

+ +生成指标可以评估生成器的多个方面性能: + +- **上下文利用率(Context Utilization)**:生成器有效使用上下文中相关信息的程度。 +- **对相关/不相关chunk中的噪音敏感度(Noise Sensitivity in Relevant/Irrelevant Chunks)**:生成器受相关和不相关文本块中噪音影响的程度。 +- **幻觉(Hallucination)**:生成的不在上下文中的错误信息。 +- **自身知识(Self-knowledge)**:使用模型自身知识而不是检索上下文中的内容。 +- **忠实度(Faithfulness)**:生成器使用检索上下文的程度。 + + +### RAGChecker指标总结 + +| 模块 | 指标 | 描述 | 注释 | +| -- | -- | -- | -- | +| 整体 | 精确(Precision) | 回复的正确性(correctness) | | +| | 召回(Recall) | 回复的完整性(completeness) | | +| | F1 | 回复的整体质量 | | +| 检索 | 声明召回(Claim Recall) | 检索上下文的声明级召回率 | | +| | 上下文精度(Context Precision) | 检索上下文中相关chunk的比例 | | +| 生成 | 上下文利用率(Context Utilization) | 生成器有效使用上下文中相关信息的程度 | | +| | 对相关chunk中的噪音敏感度(Noise Sensitivity in Relevant) | 生成器受相关chunk中噪音影响的程度 | 越低越好 | +| | 对不相关chunk中的噪音敏感度(Noise Sensitivity in Irrelevant) | 生成器受不相关chunk中噪音影响的程度 | 越低越好 | +| | 幻觉(Hallucination) | 生成器编造的错误信息 | 越低越好 | +| | 自身知识(Self-knowledge) | 使用模型自身知识而不是上下文的情况 | 是否越低越好取决于用户偏好 | +| | 忠实度(Faithfulness) | 生成器使用检索上下文的程度 | | + + +## 如何使用RAGChecker + +首先安装依赖: + +```bash +pip install ragchecker +python -m spacy download en_core_web_sm +``` + + +### 命令行运行RAGChecker + +1. 按照所需格式准备您的输入JSON文件(参见上面的描述)。 + +2. 运行评估脚本(使用我们仓库中的示例文件): +```bash +ragchecker-cli \ + --input_path=examples/checking_inputs.json \ # 这是您的输入数据 + --output_path=examples/checking_outputs.json \ # 此指定输出文件 + --extractor_name=bedrock/meta.llama3-1-70b-instruct-v1:0 \ # 关于如何设置模型,请参考下面的描述 + --checker_name=bedrock/meta.llama3-1-70b-instruct-v1:0 \ # 关于如何设置模型,请参考下面的描述 + --batch_size_extractor=64 \ + --batch_size_checker=128 \ # 可以大于batch_size_extractor + --metrics all_metrics # all_metrics, overall_metrics, retriever_metrics 或 generator_metrics +``` + +> 💡 **为Extractor和Checker设置LLM** +> +> RAGChecker使用[litellm](https://docs.litellm.ai/) 来调用LLM。请参考他们的文档了解不同的LLM提供商(如OpenAI、AWS Bedrock、AWS Sagemaker或由vllm部署): [Supported Model & Providers](https://docs.litellm.ai/docs/providers). +> +> 您也可以参考[RefChecker的相关指南](https://github.com/amazon-science/RefChecker/tree/main?tab=readme-ov-file#choose-models-for-the-extractor-and-checker) 来设置LLM。 + +### 在Python代码中使用RAGChecker + +```python +from ragchecker import RAGResults, RAGChecker +from ragchecker.metrics import all_metrics + + +# 从json/dict初始化ragresults +with open("examples/checking_inputs.json") as fp: + rag_results = RAGResults.from_json(fp.read()) + +# 设置评估器 +evaluator = RAGChecker( + extractor_name="bedrock/meta.llama3-1-70b-instruct-v1:0", + checker_name="bedrock/meta.llama3-1-70b-instruct-v1:0", + batch_size_extractor=32, + batch_size_checker=32 +) + +# 使用选定的指标(如retriever_metrics、generator_metrics、all_metrics)评估结果 +evaluator.evaluate(rag_results, all_metrics) +print(rag_results) +``` + +> 💡 **使用您自己的LLM进行声明抽取和检查** +> +> 如上所述,我们使用litellm来调用LLM进行声明提取和检查。但是,有时我们想使用自己部署的与litellm不兼容的LLM,我们可以使用参数`custom_llm_api_func`来设置调用我们的LLM的函数。 +> +>```python +># 定义调用您自己的LLM的函数 +>def my_llm_api_func(prompts): +> """ +> Get responses from LLM for the input prompts +> Parameters +> ---------- +> prompts: List[str] +> A list of prompts. +> +> Returns +> ---------- +> response_list : List[str] +> A list of generated text. +> """ +> # 在这里编写调用LLM的代码 +> # 将模型回复放入`response_list` +> # 注意:为加速评估,最好使用批处理 +> +> return response_list +> +> +># 使用自定义API函数初始化评估器 +>evaluator = RAGChecker( +> custom_llm_api_func=my_llm_api_func +>) +>``` +> +>请注意,RAGChecker将调用许多请求,因此您应该在函数中实现`批量调用`。如果您的工具不能直接支持批量调用,请考虑使用[asyncio](https://docs.python.org/3/library/asyncio.html) 做并发调用。 + + +代码成功运行后,它将输出如下所示的指标值: + +```json +Results for examples/checking_outputs.json: +{ + "overall_metrics": { + "precision": 73.3, + "recall": 62.5, + "f1": 67.3 + }, + "retriever_metrics": { + "claim_recall": 61.4, + "context_precision": 87.5 + }, + "generator_metrics": { + "context_utilization": 87.5, + "noise_sensitivity_in_relevant": 22.5, + "noise_sensitivity_in_irrelevant": 0.0, + "hallucination": 4.2, + "self_knowledge": 25.0, + "faithfulness": 70.8 + } +} +``` + + +## 解读结果并改进您的RAG系统 + +运行RAGChecker后,您将收到一组指标。以下是如何解读这些结果并采取行动: + +1. **检索模块**: + - 声明召回率低:考虑使用更先进的检索模型或增加检索chunk的数量。 + - 上下文精确率低:尝试减少检索文本块的数量以降低噪声。 + +如果可能的话,您还可以考虑微调检索器。 + +2. **生成模块**: + - 忠实度低或幻觉高:调整您的提示以强调仅使用检索到的信息。 + - 上下文利用率低:修改提示以鼓励生成器识别和使用相关信息。 + - 噪音敏感度高:在提示中增加推理步骤,帮助生成器忽略不相关信息。 + +3. **整体系统改进**: + - 尝试不同的文本块大小和检索文本块数量。 + - 尝试不同的检索器和生成器组合。 + - 如果可能,在特定领域数据上微调两个组件。 + +## FAQ + +**Q: RAGChecker可以用于非英语语言吗?** + +A: 虽然RAGChecker设计用于英语,但如果您使用能力强的多语言LLM进行声明抽取和检查就可以直接适用于其他语言。 + +**Q: RAGChecker适合生产环境监控吗?** + +A: 对于生产环境监控,请忠实度指标(faithfulness),因为它不需要标准答案。 + +**Q: RAGChecker支持自定义指标吗?** + +A: 目前,RAGChecker不支持自定义指标。考虑fork项目以添加您自己的指标,或使用独立工具进行额外评估。 \ No newline at end of file