From 2751297bb5f9dc43edfdc82df4b603be2fd5256e Mon Sep 17 00:00:00 2001 From: Eureka Date: Thu, 3 Apr 2025 15:14:43 +0800 Subject: [PATCH] GraphAGE --- .../zh/docs/DataVec/DataVec-tutorials.md | 1 + .../DataVec/figures/openGauss-AGEGraph.png | Bin 0 -> 32737 bytes .../zh/docs/DataVec/openGauss-AGEGraph.md | 214 ++++++++++++++++++ content/docs-lite/zh/menu/index.md | 1 + content/zh/docs/DataVec/DataVec-tutorials.md | 1 + .../DataVec/figures/openGauss-AGEGraph.png | Bin 0 -> 32737 bytes content/zh/docs/DataVec/openGauss-AGEGraph.md | 214 ++++++++++++++++++ content/zh/menu/index.md | 1 + 8 files changed, 432 insertions(+) create mode 100644 content/docs-lite/zh/docs/DataVec/figures/openGauss-AGEGraph.png create mode 100644 content/docs-lite/zh/docs/DataVec/openGauss-AGEGraph.md create mode 100644 content/zh/docs/DataVec/figures/openGauss-AGEGraph.png create mode 100644 content/zh/docs/DataVec/openGauss-AGEGraph.md diff --git a/content/docs-lite/zh/docs/DataVec/DataVec-tutorials.md b/content/docs-lite/zh/docs/DataVec/DataVec-tutorials.md index fdf3cc46f..8932c25af 100644 --- a/content/docs-lite/zh/docs/DataVec/DataVec-tutorials.md +++ b/content/docs-lite/zh/docs/DataVec/DataVec-tutorials.md @@ -5,3 +5,4 @@ - [打破AI黑盒，拥抱开源力量：基于openGauss+DeepSeek的本地知识库，打造你的专属AI助手！](openGauss-RAG实践.md) - [openGauss DataVec + Dify, 快速搭建你的智能助手平台](openGauss-Dify.md) - [SpringBoot 集成 openGauss DataVec， 实现向量化检索](openGauss-Springboot.md) +- [openGauss AGEGraph + 大模型实现 GraphRAG，助力更强 RAG](openGauss-AGEGraph.md) diff --git a/content/docs-lite/zh/docs/DataVec/figures/openGauss-AGEGraph.png b/content/docs-lite/zh/docs/DataVec/figures/openGauss-AGEGraph.png new file mode 100644 index 0000000000000000000000000000000000000000..b2a00fb186c0c91df15ada70d4469017d10f423f GIT binary patch literal 32737 zcmc$`cRbba|37|^l|4F?8Ch|R?7b<3$mm!}a*!Re$-8`Sq>v~*|>oM;4`{P9FYO9l-zjPh~fsoy}uA&El zoRfh-aB7Kg!T)51QoBJQ0k3bUDBgZ-{(YR}(d{qw@|!hJ>+CG}RzpWg&Pu-QRfh7< 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zF`QSxOpy2~Wzs%P`;cyxuZ4VJsshrfskq>q>%o-F-tYWmEr&1Qd7 zK<_+Z7;on0cIlTA!7!`j-i!>K^wuzy{&(z?FYlB(f2^3)ytml?aLR4U?j4XM`f2$l zF$!hMnr#DlO1=}BG^SL0c7lxJG*2|%wertBWFXPlq1GCT;E`_=H6` zGlRE1ko2f4<8-LBE|jnAR!mDV-`5b(a{ZKvNB>qUzRyfC|c9at1tRI+jq~g-@|1axYDyggQU88aCwX3}S_B)c0$4JZUq%K8w;zUB54HS= z)uPzDb@TS^+rJGYN2r~muTP6_7z_w#_|aFePS4WQ&n+#D*7C0fC-~cgy;+lLw9fo| zOmo#!3Shzsd?t2a70%8^jmF5Z1H>sA3>mx`dGaU$#OwgVY&+|d<{vhEy5<0RcQAV^ zhLn^P2p)elWwTnq7=O9-lA`_2F~luz81?JH_UED{W{ole0^?h|Mkti~(${?oiUa5N zUAm!@Gi$!)J%mGLYXcXoq<%g$gl=#{dXt+wKQ}kGpdh9pnkmLFX4E3-7-C!ucAThA zP6Zh`dEUo(D8dYiEV4z0Tc6e%pGj|-HOj8N^(+>r=-mC>h}_#}@R#!4aRsR!_IS|K zfr#HM$1Kn6rCCu8yj=#m!@t|g{TL4sLa0Oxt`Y7vo)_`@^_R&Ne1Q>xT$Y1eB0g!B zeU|$3zO2%{U%p(d?;9B@$~F}H*OY2Z$Nvtbmbl<+HbMCRPqikPew7<{_p~UCz2$bU zcxM(DYj`W|D0{2ysHU{boB!2;Eghn;*CR~X5EXI9dFD6 zvMrR2yNZm00@o!)N!)tYR{+gonisv_8Aw5432/tcp opengauss-age + +``` +#### 1.2 AGE插件安装 + +``` +#进入openGauss容器中 253714c9c869为 CONTAINER ID +docker exec -it 253714c9c869 bash + +#切换到omm用户 +su omm + +#omm用户，连接数据库，默认使用omm数据库 +gsql -r + +#连接数据库后，omm数据库安装age插件 +create extension age; + +#退出数据库连接 +\q + +``` + +#### 1.3 python依赖安装 + +``` +pip install -U langchain_community langchain langgraph langchain-ollama langchain-experimental langchain-openai +``` +下载已适配的 langchain_community 仓库，操作如下： + +##### 1.3.1 下载适配好的langchain_community仓 +``` +git clone https://gitee.com/lin-qiang123/langchain.git +``` +##### 1.3.2 查找pip 安装的langchain_community位置 +``` +pip show langchain_community +``` +输出 + +``` +Name: langchain-community +Version: 0.3.20 +Summary: Community contributed LangChain integrations. +Home-page: +Author: +Author-email: +License: MIT +Location: c:\users\test\appdata\local\programs\python\python310\lib\site-packages #依赖库安装的位置 +Requires: numpy, PyYAML, aiohttp, tenacity, dataclasses-json, langchain, httpx-sse, SQLAlchemy, pydantic-settings, langchain-core, langsmith, requests +Required-by: langchain-experimental +``` + +##### 1.3.3 替换langchain-community +在git clone 下来的代码仓中，找到langchain\libs\community\langchain_community文件夹，复制并且覆盖到`Location`显示的文件夹中 + + +### 2. 利用 模型和 openGauss AGEGraph 快速构建知识图谱和检索 +本文使用百炼大模型，取得api-key，并且配置DASHSCOPE_API_KEY环境变量 +#### 2.1 初始化大模型 + +``` +from langchain_community.llms import Tongyi +import os + +os.environ["DASHSCOPE_API_KEY"] = "sk-**" +graph_llm =Tongyi(model="qwen-plus", temperature=0, base_url="https://dashscope.aliyuncs.com/compatible-mode/v1") + +``` +#### 2.2 通过大模型提取文本数据中的实体和关系 + + +``` +from langchain_core.documents import Document +from langchain_experimental.graph_transformers import LLMGraphTransformer + +#设置需要提取的实体类型和关系 +llm_transformer = LLMGraphTransformer( + llm=graph_llm, + allowed_nodes=["Person", "Organization", "Location", "Award", "ResearchField"], + allowed_relationships = ["SPOUSE", "AWARD", "FIELD_OF_RESEARCH", "WORKS_AT", "IN_LOCATION"], +) + +text = """ +Marie Curie, 7 November 1867 – 4 July 1934, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity. +She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields. +Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes. +She was, in 1906, the first woman to become a professor at the University of Paris. +Also, Robin Williams. +""" + +# 将文本转化为图 +documents = [Document(page_content=text)] +graph_documents = llm_transformer.convert_to_graph_documents(documents) + + +print(f"Nodes from graph doc:{graph_documents[0].nodes}") +print(f"Relationships from graph doc:{graph_documents[0].relationships}") +``` +输出 + +``` +Nodes from graph doc:[Node(id='Nobel Prize', type='Award', properties={}), Node(id='University of Paris', type='Organization', properties={}), Node(id='Marie Curie', type='Person', properties={}), Node(id='radioactivity', type='ResearchField', properties={}), Node(id='Pierre Curie', type='Person', properties={})] +Relationships from graph doc:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='radioactivity', type='ResearchField', properties={}), type='FIELD_OF_RESEARCH', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University of Paris', type='Organization', properties={}), type='WORKS_AT', properties={})] +``` +#### 2.3 实例化 openGauss AGEGraph 客户端，持久化图数据 + +``` +from langchain_community.graphs.age_graph import AGEGraph + +conf = { + "database": "omm", + "user": "gaussdb", + "password": "YourPassoword", + "host": "Your IP", + "port": 8888, + "sslmode": "disable" +} +graph=AGEGraph(graph_name='graph_test1',conf=conf,create=True) +graph.add_graph_documents(graph_documents) +graph.refresh_schema() + +``` + +#### 2.4 大模型 Text2Cypher：提取问题关键词并生成图检索语句 + +通过增加提示词`cypher_prompt`，可以让大模型生成的图查询语句更加精准 +``` +from langchain_core.prompts import PromptTemplate +from langchain.chains import GraphCypherQAChain + +cypher_prompt = PromptTemplate( + template="""你是 AGE Cypher 查询生成的专家。 + 使用以下架构生成一个 Cypher 查询，以回答给定问题。 + 不要出现name和properties和cypher + + 架构： + {schema} + + 问题：{question} + + Cypher 查询：""", + input_variables=["schema", "question"], +) + +chain = GraphCypherQAChain.from_llm( + graph_llm, graph=graph, verbose=True, allow_dangerous_requests=True, cypher_validation=True, return_intermediate_steps=True,cypher_prompt=cypher_prompt +) + +question = "Who get Nobel Prize ?" +result = chain.invoke({"query": question}) +``` +输出 + +> Entering new GraphCypherQAChain chain... +Generated Cypher: +MATCH (p:Person)-[:AWARD]->(a:Award) +WHERE a.id = "Nobel Prize" +RETURN p.id +Full Context: +[{'p_id': 'Pierre Curie'}, {'p_id': 'Marie Curie'}] + +### 3. 大模型依据检索的图数据生成回答 + +#### 3.1 设置提示词 +``` +prompt = PromptTemplate( + template="""You are an assistant for question-answering tasks. + Use the following pieces of retrieved context from a graph database to answer the question. If you don't know the answer, just say that you don't know. + Use two sentences maximum and keep the answer concise: + Question: {question} + Graph Context: {graph_context} + Answer: + """, + input_variables=["question", "graph_context"], +) + +``` +#### 3.2 生成回答 +``` +from langchain_core.output_parsers import StrOutputParser + +composite_chain = prompt | graph_llm |StrOutputParser() + +answer = composite_chain.invoke( + {"question": question, "graph_context": result} +) +print(answer) +``` +输出 + +``` +Marie Curie and Pierre Curie received the Nobel Prize. They were recognized for their groundbreaking work in radioactivaity. +``` + +### 4. 总结 +结合大模型和 openGauss AGEGraph 图数据库，我们可以快速进行知识图谱的生成、持久化以及检索，完成 GraphRAG 的搭建。此外，当前 openGauss 还支持标量、向量类型存储与检索，只需部署 openGauss 数据库，即可实现支持标量、向量、知识图谱多路召回的更强 RAG 系统。 diff --git a/content/docs-lite/zh/menu/index.md b/content/docs-lite/zh/menu/index.md index e27be8fd3..76bd9cfab 100644 --- a/content/docs-lite/zh/menu/index.md +++ b/content/docs-lite/zh/menu/index.md @@ -650,6 +650,7 @@ headless: true - [打破AI黑盒，拥抱开源力量：基于openGauss+DeepSeek的本地知识库，打造你的专属AI助手！]({{< relref "./docs/DataVec/openGauss-RAG实践.md" >}}) - [openGauss Datavec + Dify，快速搭建你的智能助手平台]({{< relref "./docs/DataVec/openGauss-Dify.md" >}}) - [Spring Boot集成openGauss DataVec实现向量化检索]({{< relref "./docs/DataVec/openGauss-Springboot.md" >}}) + - [openGauss AGEGraph + 大模型实现 GraphRAG，助力更强 RAG]({{< relref "./docs/DataVec/openGauss-AGEGraph.md" >}}) - [安全加固指南]({{< relref "./docs/SecHarden/secHarden.md" >}}) - [数据库加固概述]({{< relref "./docs/SecHarden/数据库加固概述.md" >}}) - [安全配置规范基线]({{< relref "./docs/SecHarden/安全配置规范基线.md" >}}) diff --git a/content/zh/docs/DataVec/DataVec-tutorials.md b/content/zh/docs/DataVec/DataVec-tutorials.md index fdf3cc46f..8932c25af 100644 --- a/content/zh/docs/DataVec/DataVec-tutorials.md +++ b/content/zh/docs/DataVec/DataVec-tutorials.md @@ -5,3 +5,4 @@ - [打破AI黑盒，拥抱开源力量：基于openGauss+DeepSeek的本地知识库，打造你的专属AI助手！](openGauss-RAG实践.md) - [openGauss DataVec + Dify, 快速搭建你的智能助手平台](openGauss-Dify.md) - [SpringBoot 集成 openGauss DataVec， 实现向量化检索](openGauss-Springboot.md) +- [openGauss AGEGraph + 大模型实现 GraphRAG，助力更强 RAG](openGauss-AGEGraph.md) diff --git a/content/zh/docs/DataVec/figures/openGauss-AGEGraph.png b/content/zh/docs/DataVec/figures/openGauss-AGEGraph.png new file mode 100644 index 0000000000000000000000000000000000000000..b2a00fb186c0c91df15ada70d4469017d10f423f GIT binary patch literal 32737 zcmc$`cRbba|37|^l|4F?8Ch|R?7b<3$mm!}a*!Re$-8`Sq>v~*|>oM;4`{P9FYO9l-zjPh~fsoy}uA&El 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+#omm用户，连接数据库，默认使用omm数据库 +gsql -r + +#连接数据库后，omm数据库安装age插件 +create extension age; + +#退出数据库连接 +\q + +``` + +#### 1.3 python依赖安装 + +``` +pip install -U langchain_community langchain langgraph langchain-ollama langchain-experimental langchain-openai +``` +下载已适配的 langchain_community 仓库，操作如下： + +##### 1.3.1 下载适配好的langchain_community仓 +``` +git clone https://gitee.com/lin-qiang123/langchain.git +``` +##### 1.3.2 查找pip 安装的langchain_community位置 +``` +pip show langchain_community +``` +输出 + +``` +Name: langchain-community +Version: 0.3.20 +Summary: Community contributed LangChain integrations. +Home-page: +Author: +Author-email: +License: MIT +Location: c:\users\test\appdata\local\programs\python\python310\lib\site-packages #依赖库安装的位置 +Requires: numpy, PyYAML, aiohttp, tenacity, dataclasses-json, langchain, httpx-sse, SQLAlchemy, pydantic-settings, langchain-core, langsmith, requests +Required-by: langchain-experimental +``` + +##### 1.3.3 替换langchain-community +在git clone 下来的代码仓中，找到langchain\libs\community\langchain_community文件夹，复制并且覆盖到`Location`显示的文件夹中 + + +### 2. 利用 模型和 openGauss AGEGraph 快速构建知识图谱和检索 +本文使用百炼大模型，取得api-key，并且配置DASHSCOPE_API_KEY环境变量 +#### 2.1 初始化大模型 + +``` +from langchain_community.llms import Tongyi +import os + +os.environ["DASHSCOPE_API_KEY"] = "sk-**" +graph_llm =Tongyi(model="qwen-plus", temperature=0, base_url="https://dashscope.aliyuncs.com/compatible-mode/v1") + +``` +#### 2.2 通过大模型提取文本数据中的实体和关系 + + +``` +from langchain_core.documents import Document +from langchain_experimental.graph_transformers import LLMGraphTransformer + +#设置需要提取的实体类型和关系 +llm_transformer = LLMGraphTransformer( + llm=graph_llm, + allowed_nodes=["Person", "Organization", "Location", "Award", "ResearchField"], + allowed_relationships = ["SPOUSE", "AWARD", "FIELD_OF_RESEARCH", "WORKS_AT", "IN_LOCATION"], +) + +text = """ +Marie Curie, 7 November 1867 – 4 July 1934, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity. +She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields. +Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes. +She was, in 1906, the first woman to become a professor at the University of Paris. +Also, Robin Williams. +""" + +# 将文本转化为图 +documents = [Document(page_content=text)] +graph_documents = llm_transformer.convert_to_graph_documents(documents) + + +print(f"Nodes from graph doc:{graph_documents[0].nodes}") +print(f"Relationships from graph doc:{graph_documents[0].relationships}") +``` +输出 + +``` +Nodes from graph doc:[Node(id='Nobel Prize', type='Award', properties={}), Node(id='University of Paris', type='Organization', properties={}), Node(id='Marie Curie', type='Person', properties={}), Node(id='radioactivity', type='ResearchField', properties={}), Node(id='Pierre Curie', type='Person', properties={})] +Relationships from graph doc:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='radioactivity', type='ResearchField', properties={}), type='FIELD_OF_RESEARCH', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University of Paris', type='Organization', properties={}), type='WORKS_AT', properties={})] +``` +#### 2.3 实例化 openGauss AGEGraph 客户端，持久化图数据 + +``` +from langchain_community.graphs.age_graph import AGEGraph + +conf = { + "database": "omm", + "user": "gaussdb", + "password": "YourPassoword", + "host": "Your IP", + "port": 8888, + "sslmode": "disable" +} +graph=AGEGraph(graph_name='graph_test1',conf=conf,create=True) +graph.add_graph_documents(graph_documents) +graph.refresh_schema() + +``` + +#### 2.4 大模型 Text2Cypher：提取问题关键词并生成图检索语句 + +通过增加提示词`cypher_prompt`，可以让大模型生成的图查询语句更加精准 +``` +from langchain_core.prompts import PromptTemplate +from langchain.chains import GraphCypherQAChain + +cypher_prompt = PromptTemplate( + template="""你是 AGE Cypher 查询生成的专家。 + 使用以下架构生成一个 Cypher 查询，以回答给定问题。 + 不要出现name和properties和cypher + + 架构： + {schema} + + 问题：{question} + + Cypher 查询：""", + input_variables=["schema", "question"], +) + +chain = GraphCypherQAChain.from_llm( + graph_llm, graph=graph, verbose=True, allow_dangerous_requests=True, cypher_validation=True, return_intermediate_steps=True,cypher_prompt=cypher_prompt +) + +question = "Who get Nobel Prize ?" +result = chain.invoke({"query": question}) +``` +输出 + +> Entering new GraphCypherQAChain chain... +Generated Cypher: +MATCH (p:Person)-[:AWARD]->(a:Award) +WHERE a.id = "Nobel Prize" +RETURN p.id +Full Context: +[{'p_id': 'Pierre Curie'}, {'p_id': 'Marie Curie'}] + +### 3. 大模型依据检索的图数据生成回答 + +#### 3.1 设置提示词 +``` +prompt = PromptTemplate( + template="""You are an assistant for question-answering tasks. + Use the following pieces of retrieved context from a graph database to answer the question. If you don't know the answer, just say that you don't know. + Use two sentences maximum and keep the answer concise: + Question: {question} + Graph Context: {graph_context} + Answer: + """, + input_variables=["question", "graph_context"], +) + +``` +#### 3.2 生成回答 +``` +from langchain_core.output_parsers import StrOutputParser + +composite_chain = prompt | graph_llm |StrOutputParser() + +answer = composite_chain.invoke( + {"question": question, "graph_context": result} +) +print(answer) +``` +输出 + +``` +Marie Curie and Pierre Curie received the Nobel Prize. They were recognized for their groundbreaking work in radioactivaity. +``` + +### 4. 总结 +结合大模型和 openGauss AGEGraph 图数据库，我们可以快速进行知识图谱的生成、持久化以及检索，完成 GraphRAG 的搭建。此外，当前 openGauss 还支持标量、向量类型存储与检索，只需部署 openGauss 数据库，即可实现支持标量、向量、知识图谱多路召回的更强 RAG 系统。 diff --git a/content/zh/menu/index.md b/content/zh/menu/index.md index 8ae385e31..398ffd468 100644 --- a/content/zh/menu/index.md +++ b/content/zh/menu/index.md @@ -935,6 +935,7 @@ headless: true - [打破AI黑盒，拥抱开源力量：基于openGauss+DeepSeek的本地知识库，打造你的专属AI助手！]({{< relref "./docs/DataVec/openGauss-RAG实践.md" >}}) - [openGauss Datavec + Dify，快速搭建你的智能助手平台]({{< relref "./docs/DataVec/openGauss-Dify.md" >}}) - [Spring Boot集成openGauss DataVec实现向量化检索]({{< relref "./docs/DataVec/openGauss-Springboot.md" >}}) + - [openGauss AGEGraph + 大模型实现 GraphRAG，助力更强 RAG]({{< relref "./docs/DataVec/openGauss-AGEGraph.md" >}}) - [AI特性指南]({{< relref "./docs/AIFeatureGuide/AI特性.md" >}}) - [AI4DB: 数据库自治运维]({{< relref "./docs/AIFeatureGuide/AI4DB-数据库自治运维.md" >}}) - [DBMind模式说明]({{< relref "./docs/AIFeatureGuide/DBMind模式说明.md" >}}) -- Gitee