From 3357da74954cbcad2c84b99f3f05bd57a88b8b02 Mon Sep 17 00:00:00 2001 From: yanqiangmiffy <1185918903@qq.com> Date: Tue, 10 Sep 2024 16:12:53 +0800 Subject: [PATCH 1/3] =?UTF-8?q?apps@=E6=B7=BB=E5=8A=A0GoMate=E6=A1=88?= =?UTF-8?q?=E4=BE=8B=EF=BC=9A=E8=AE=AF=E9=A3=9E=E5=A4=A7=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?RAG=E6=99=BA=E8=83=BD=E9=97=AE=E7=AD=94=E6=8C=91=E6=88=98?= =?UTF-8?q?=E8=B5=9B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/rag.md | 30 ++++++++++---------- examples/rag/eda.py | 49 +++++++++++++++++++++++++++++++++ examples/rag/xunfei_rag.py | 28 ++++++++++++------- gomate/modules/generator/llm.py | 2 +- 4 files changed, 83 insertions(+), 26 deletions(-) create mode 100644 examples/rag/eda.py diff --git a/docs/rag.md b/docs/rag.md index fcb69c8..bd3d4f6 100644 --- a/docs/rag.md +++ b/docs/rag.md @@ -20,18 +20,18 @@ #### 3.提交结果 -| 序号 | 方法 | 分数 | -|----|----------------------------------------------|-------| -| 0 | baseline:glm4_plus | 0.34 | -| 1 | bm25s | 0.06091 | -| 2 | bm25s+修改prompt | 0.26175 | -| 3 | bm25s+hybrid检索器+qwen27b | 0.22371 | -| 4 | bm25s+hybrid检索器+qwen21.5b | 0.23608 | -| 5 | dense+qwen21.5b | 0.24613 | -| 6 | hybrid检索器 | 0.05696 | -| 7 | hybrid检索器+qwen7b +xunfei prompt | 0.33623 | -| 8 | hybrid检索器+top10+qwen7b +xunfei prompt | 0.32735 | -| 9 | hybrid检索器+top5+glm4-9b +glm4 prompt | 0.37147 | -| 10 | hybrid检索器+top5+glm4-9b +xunfei prompt | 0.41775 | -| 11 | hybrid检索器+top5+glm4-9b +xunfei prompt+【无法回答】 | 0.3878 | -| 11 | hybrid检索器+top5+glm4-9b +xunfei prompt+rerank | | +| 序号 | 方法 | 分数 | +|----|------------------------------------------------|---------| +| 0 | baseline:glm4_plus | 0.34 | +| 1 | bm25s | 0.06091 | +| 2 | bm25s+修改prompt | 0.26175 | +| 3 | bm25s+hybrid检索器+qwen27b | 0.22371 | +| 4 | bm25s+hybrid检索器+qwen21.5b | 0.23608 | +| 5 | dense+qwen21.5b | 0.24613 | +| 6 | hybrid检索器 | 0.05696 | +| 7 | hybrid检索器+qwen7b +xunfei prompt | 0.33623 | +| 8 | hybrid检索器+top10+qwen7b +xunfei prompt | 0.32735 | +| 9 | hybrid检索器+top5+glm4-9b +glm prompt | 0.37147 | +| 10 | hybrid检索器+top5+glm4-9b +xunfei prompt | 0.41775 | +| 11 | hybrid检索器+top5+glm4-9b +xunfei prompt+【无法回答】 | 0.3878 | +| 12 | hybrid检索器+top5+glm4-9b +qwen prompt+rerank :失误 | 0.27884 | diff --git a/examples/rag/eda.py b/examples/rag/eda.py new file mode 100644 index 0000000..6576fe4 --- /dev/null +++ b/examples/rag/eda.py @@ -0,0 +1,49 @@ +import pandas as pd +import matplotlib.pyplot as plt +from collections import Counter +import jieba + +# 读取数据 +news = [] +with open('../../data/competitions/xunfei/corpus.txt', 'r', encoding='utf-8') as f: + for line in f.readlines(): + news.append(line.strip()) + +df = pd.DataFrame({'news': news}) + +# 1. 查看数据的基本信息 +print("数据的基本信息:") +print(df.info()) +print("\n数据的统计摘要:") +print(df.describe()) + +# 2. 新闻长度分析 +df['news_length'] = df['news'].apply(len) + +# 可视化新闻长度分布 +plt.figure(figsize=(10, 6)) +plt.hist(df['news_length'], bins=50, color='skyblue') +plt.title('new length') +plt.xlabel('length of chars') +plt.ylabel('nums of news') +plt.show() + +# 3. 词频分析 +# 使用jieba分词 +df['news_words'] = df['news'].apply(lambda x: jieba.lcut(x)) + +# 统计词频 +all_words = [word for words in df['news_words'] for word in words if len(word.strip())>2] +word_counts = Counter(all_words) + +# 查看最常见的10个词 +common_words = word_counts.most_common(20) +print("\n新闻中最常见的10个词:") +for word, count in common_words: + print(f"{word}: {count} 次") + +# 4. 假设新闻来源是含有"记者"等关键词的新闻行 +df['source'] = df['news'].apply(lambda x: '记者' in x or '本报讯' in x) + +# 查看有新闻来源的条数 +print("\n含有新闻来源的条数:", df['source'].sum()) diff --git a/examples/rag/xunfei_rag.py b/examples/rag/xunfei_rag.py index e86cfed..7538aa3 100644 --- a/examples/rag/xunfei_rag.py +++ b/examples/rag/xunfei_rag.py @@ -7,7 +7,7 @@ from gomate.modules.document.txt_parser import TextParser from gomate.modules.document.utils import PROJECT_BASE from gomate.modules.generator.llm import GLM4Chat -from gomate.modules.reranker.bge_reranker import BgeReranker, BgeRerankerConfig +from gomate.modules.reranker.bge_reranker import BgeRerankerConfig, BgeReranker from gomate.modules.retrieval.bm25s_retriever import BM25RetrieverConfig from gomate.modules.retrieval.dense_retriever import DenseRetrieverConfig from gomate.modules.retrieval.hybrid_retriever import HybridRetriever, HybridRetrieverConfig @@ -36,12 +36,15 @@ def generate_chunks(): test_path = "/data/users/searchgpt/yq/GoMate_dev/data/competitions/xunfei/test_question.csv" embedding_model_path = "/data/users/searchgpt/pretrained_models/bge-large-zh-v1.5" llm_model_path = "/data/users/searchgpt/pretrained_models/glm-4-9b-chat" - + # ====================文件解析+切片========================= + generate_chunks() with open(f'{PROJECT_BASE}/output/chunks.pkl', 'rb') as f: chunks = pickle.load(f) corpus = [] for chunk in chunks: corpus.extend(chunk) + + # ====================检索器配置========================= # BM25 and Dense Retriever configurations bm25_config = BM25RetrieverConfig( method='lucene', @@ -51,7 +54,6 @@ def generate_chunks(): ) bm25_config.validate() print(bm25_config.log_config()) - dense_config = DenseRetrieverConfig( model_name_or_path=embedding_model_path, dim=1024, @@ -59,34 +61,40 @@ def generate_chunks(): ) config_info = dense_config.log_config() print(config_info) - # Hybrid Retriever configuration + # 由于分数框架不在同一维度,建议可以合并 hybrid_config = HybridRetrieverConfig( bm25_config=bm25_config, dense_config=dense_config, - bm25_weight=0.7, - dense_weight=0.3 + bm25_weight=0.7, # bm25检索结果权重 + dense_weight=0.3 # dense检索结果权重 ) hybrid_retriever = HybridRetriever(config=hybrid_config) + # 构建索引 # hybrid_retriever.build_from_texts(corpus) + # 保存索引 # hybrid_retriever.save_index() + # 加载索引 hybrid_retriever.load_index() - # Query + + # ====================检索测试========================= query = "新冠肺炎疫情" results = hybrid_retriever.retrieve(query, top_k=5) - # Output results for result in results: print(f"Text: {result['text']}, Score: {result['score']}") + # ====================排序配置========================= reranker_config = BgeRerankerConfig( model_name_or_path="/data/users/searchgpt/pretrained_models/bge-reranker-large" ) bge_reranker = BgeReranker(reranker_config) + # ====================生成器配置========================= # qwen_chat = QwenChat(llm_model_path) glm4_chat = GLM4Chat(llm_model_path) + # ====================检索问答========================= test = pd.read_csv(test_path) answers = [] for question in tqdm(test['question'], total=len(test)): @@ -95,7 +103,7 @@ def generate_chunks(): query=question, documents=[doc['text'] for idx, doc in enumerate(search_docs)] ) - print(search_docs) + # print(search_docs) content = '/n'.join([f'信息[{idx}]:' + doc['text'] for idx, doc in enumerate(search_docs)]) answer = glm4_chat.chat(prompt=question, content=content) answers.append(answer[0]) @@ -104,4 +112,4 @@ def generate_chunks(): print("************************************/n") test['answer'] = answers - test[['answer']].to_csv(f'{PROJECT_BASE}/output/bm25_v2.csv', index=False) + test[['answer']].to_csv(f'{PROJECT_BASE}/output/gomate_baseline.csv', index=False) diff --git a/gomate/modules/generator/llm.py b/gomate/modules/generator/llm.py index f4dc292..81781d1 100644 --- a/gomate/modules/generator/llm.py +++ b/gomate/modules/generator/llm.py @@ -151,7 +151,7 @@ def chat(self, prompt: str, history: List = [], content: str = '', llm_only: boo if llm_only: prompt = prompt else: - prompt = PROMPT_TEMPLATE['Qwen_PROMPT_TEMPALTE'].format(question=prompt, context=content) + prompt = PROMPT_TEMPLATE['Xunfei_PROMPT_TEMPLATE'].format(question=prompt, context=content) print(prompt) inputs = self.tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True, From c164e80a1284c17a9cbf5538e45d5f225acd101d Mon Sep 17 00:00:00 2001 From: yanqiangmiffy <1185918903@qq.com> Date: Tue, 10 Sep 2024 16:21:48 +0800 Subject: [PATCH 2/3] =?UTF-8?q?apps@=E6=B7=BB=E5=8A=A0=E8=81=94=E7=B3=BB?= =?UTF-8?q?=E6=96=B9=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 36 ++++++++++++++++++++++++++++-------- examples/rag/xunfei_rag.py | 2 +- resources/wechat.png | Bin 0 -> 186088 bytes 3 files changed, 29 insertions(+), 9 deletions(-) create mode 100644 resources/wechat.png diff --git a/README.md b/README.md index aca2214..020089a 100644 --- a/README.md +++ b/README.md @@ -8,14 +8,17 @@ [![pydocstyle](https://img.shields.io/badge/pydocstyle-enabled-AD4CD3)](http://www.pydocstyle.org/en/stable/) [![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) - ## 🔥Gomate 简介 -GoMate是一款配置化模块化的Retrieval-Augmented Generation (RAG) 框架,旨在提供**可靠的输入与可信的输出**,确保用户在检索问答场景中能够获得高质量且可信赖的结果。 + +GoMate是一款配置化模块化的Retrieval-Augmented Generation (RAG) 框架,旨在提供**可靠的输入与可信的输出** +,确保用户在检索问答场景中能够获得高质量且可信赖的结果。 GoMate框架的设计核心在于其**高度的可配置性和模块化**,使得用户可以根据具体需求灵活调整和优化各个组件,以满足各种应用场景的要求。 ## 🔨Gomate框架 + ![framework.png](resources%2Fframework.png) + ## ✨主要特色 **“Reliable input,Trusted output”** @@ -24,18 +27,22 @@ GoMate框架的设计核心在于其**高度的可配置性和模块化**,使 ## 🏗️ 更新记录 -- 添加[MinerU文档解析](https://github.com/gomate-community/GoMate/blob/main/docs/mineru.md):一站式开源高质量数据提取工具,支持PDF/网页/多格式电子书提取`[20240907] ` +- 添加[MinerU文档解析](https://github.com/gomate-community/GoMate/blob/main/docs/mineru.md) + :一站式开源高质量数据提取工具,支持PDF/网页/多格式电子书提取`[20240907] ` - RAPTOR:递归树检索器实现 - 支持多种文件解析并且模块化目前支持解析的文件类型包括:`text`,`docx`,`ppt`,`excel`,`html`,`pdf`,`md`等 - 优化了`DenseRetriever`,支持索引构建,增量追加以及索引保存,保存内容包括文档、向量以及索引 - 添加`ReRank`的BGE排序、Rewriter的`HyDE` - 添加`Judge`的BgeJudge,判断文章是否有用 `20240711` + ## 🚀快速上手 ### 安装环境 + ```shell pip install -r requirements.txt ``` + ### 1 文档解析 目前支持解析的文件类型包括:`text`,`docx`,`ppt`,`excel`,`html`,`pdf`,`md` @@ -74,13 +81,12 @@ print(data.columns) retriever.build_from_texts(documents) ``` - 保存索引 + ```python retriever.save_index() ``` - ### 3 检索文档 ```python @@ -89,8 +95,10 @@ print(result) ``` ### 4 大模型问答 + ```python from gomate.modules.generator.llm import GLMChat + chat = GLMChat(path='THUDM/chatglm3-6b') print(chat.chat(question, [], content)) ``` @@ -119,7 +127,6 @@ from gomate.modules.reranker.bge_reranker import BgeReranker from gomate.modules.retrieval.dense_retriever import DenseRetriever - class RagApplication(): def __init__(self, config): pass @@ -139,9 +146,10 @@ class RagApplication(): 模块可见[rag.py](gomate/applications/rag.py) - ### 🌐体验RAG效果 + 可以配置本地模型路径 + ```text # 修改成自己的配置!!! app_config = ApplicationConfig() @@ -166,17 +174,29 @@ application.init_vector_store() ```shell python app.py ``` + 浏览器访问:[127.0.0.1:7860](127.0.0.1:7860) ![demo.png](resources%2Fdemo.png) app后台日志: ![app_logging.png](resources%2Fapp_logging.png) + ## ⭐️ Star History [![Star History Chart](https://api.star-history.com/svg?repos=gomate-community/GoMate&type=Date)](https://star-history.com/#gomate-community/GoMate&Date) ## 研究与开发团队 + 本项目由网络数据科学与技术重点实验室[`GoMate`](https://github.com/gomate-community)团队完成,团队指导老师为郭嘉丰、范意兴研究员。 -GoMate技术交流群可添加:1185918903(微信) +## 技术交流群 + +欢迎多提建议、Bad cases,欢迎进群及时交流,也欢迎大家多提PR
+ + + + +群满或者合作交流可以联系: + + diff --git a/examples/rag/xunfei_rag.py b/examples/rag/xunfei_rag.py index 7538aa3..4414d2c 100644 --- a/examples/rag/xunfei_rag.py +++ b/examples/rag/xunfei_rag.py @@ -37,7 +37,7 @@ def generate_chunks(): embedding_model_path = "/data/users/searchgpt/pretrained_models/bge-large-zh-v1.5" llm_model_path = "/data/users/searchgpt/pretrained_models/glm-4-9b-chat" # ====================文件解析+切片========================= - generate_chunks() + # generate_chunks() with open(f'{PROJECT_BASE}/output/chunks.pkl', 'rb') as f: chunks = pickle.load(f) corpus = [] diff --git a/resources/wechat.png b/resources/wechat.png new file mode 100644 index 0000000000000000000000000000000000000000..567b3f9dd97ba75dfbfd233fcf531a185cdc0bbc GIT binary patch literal 186088 zcmd?Q1yfwj7e1Jf1a}GU8r)qHoWb3l2{6duE(z`gcXxLkB)Ge~dvG6Yd4K=it^E#L zHC279>(-sR_jI3g9_dge1t}Cn0>lp=KA^}*i>rM202B1#!zV^~nD-e;yc^69AAWt1 z5&xm)X7FzfPA$&$?ZWqqC?*WN-!@4_8Hl)|LA}gbqinua`SV8!eD=?)p3$xMl1rZF z?L12Y3mxoBgsv}!xHo5OD;wA8*H2m3f(Mu8omD=m{pg~c2R9iL31lzA?|=2-!v(_O z{pFYcT|Reyh(-SID(mwfv;W2is$W!}-p9YRAF1&Fn-E4I8~Sg&koh13`#%0sgpp?d zZ^8@nd+f*eaWn4!=Z|=HeYwmMe!X~8C0%QJD~J|3=zMsAke-XQJvqL*y}d@SL!etW=hWvL zGo&xq`>%)XSB6=SQz-W@`>$E$AtJkNQx7IP= zm+?36$WDEv55nf}f&1>Mm~div$l3nJ0ReAxoc{Y~4FXLc*3-#ke~v=Oi!g-`%i;Zj zCYenaMZpWR!|S(m(FvPVd2=wS`C!Eg(`AQOVEa+3_?ix(i}y1YtEh^9Ht2IOhG^KP z!{=ahWC^8X*>k|{KP3SdSky<%Fl8}^pWBP8pYD$ z66o}PY8brYWatP#sE{$}#3xFWgxvPgpYKeJ@MsJ|^Z zTRr5x>ndByYM%5LAZXyj;H^_QXeapf;o?sIoaMu29o+k(ukTD;@4!@*@p-%gYHB8R z8T~TG^CQEPAfwmDlSGk|2P++s+*7wS4Xf?Jw+7!lpU27#B!_+dp~6?>u(D#xKZGtv zi!WIW&83@4(k;KgLrA5TvdtHkG{c>A!nLCW;uLGJha5*%1>_gt8n$(sYI)8pWnHt5Kse-8Ky@KC}5P^TQT zwV&s<6B-*@)x43)6fWPG$8N0`JWRYagNq6CP2~;}&)7-}f>G;IU&EXh4uu+;f~juF zk>`vxY*k=uJX=rRaI)1n~I}H{&B=M*E^!E9`HUD&C#5K2bcjuC! 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z!9wZLz;1b1z~?iZ!TCz5?*xYAUf!Up&$Gf6y_t@9e6eLnXZ4_czhJ4Xf#152$5P~Q zFQlU7`z6;sJKGnLR~(;I2N&v(r^sKk6CV|G%U&nOzK~1*t=i`Jcl+tLKaG)N;!L!K zSF&DY*KV&DHI}8k3~~78U(hd0%k`iwpj74N$@@7b<@ZjQ9<0_Di?WiUbdOKJPCnCY zcS0`otwJhp;PlHL5emMu!ju|xf?NnZ3khKydyXdhn2sDGlb?VjQl&E@&`E9xjOPFM bZ}~wzUl+kaMWRthz{h!AL!CM;r`Z1i`wsJp literal 0 HcmV?d00001 From 0a6612f1047d56ce1a3041c0abfafda8efcfdd95 Mon Sep 17 00:00:00 2001 From: yanqiangmiffy <1185918903@qq.com> Date: Tue, 10 Sep 2024 16:22:59 +0800 Subject: [PATCH 3/3] =?UTF-8?q?apps@=E6=B7=BB=E5=8A=A0=E8=81=94=E7=B3=BB?= =?UTF-8?q?=E6=96=B9=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 020089a..3a7623c 100644 --- a/README.md +++ b/README.md @@ -194,7 +194,7 @@ app后台日志: 欢迎多提建议、Bad cases,欢迎进群及时交流,也欢迎大家多提PR
- + 群满或者合作交流可以联系: