diff --git a/content/docs-lite/zh/docs/DataVec/DataVec-tutorials.md b/content/docs-lite/zh/docs/DataVec/DataVec-tutorials.md index fdf3cc46fc0a8a80687b324ef900b27759ab7af1..8932c25af747c33cf31b36e87c79f7846e95125e 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 Binary files /dev/null and b/content/docs-lite/zh/docs/DataVec/figures/openGauss-AGEGraph.png differ diff --git a/content/docs-lite/zh/docs/DataVec/openGauss-AGEGraph.md b/content/docs-lite/zh/docs/DataVec/openGauss-AGEGraph.md new file mode 100644 index 0000000000000000000000000000000000000000..a52f2c4663de820e6588859aff576a6e03d4369b --- /dev/null +++ b/content/docs-lite/zh/docs/DataVec/openGauss-AGEGraph.md @@ -0,0 +1,214 @@ +# openGauss AGEGraph + 大模型实现 GraphRAG，助力更强 RAG + +传统 RAG（检索增强生成）通常使用向量数据库检索相关文档来提高大模型的问题回答准确性，但是基于向量的 RAG 不能够很好的表达事物之间的联系。而GraphRAG基于知识图谱的检索增强生成，能够提供结构化检索能力，将图作为传统的RAG多路召回的一环，使知识表达更易解释、对复杂关系更易支持。 + +目前 [openGauss AGEGraph](https://gitee.com/opengauss/Plugin/blob/master/contrib/age/README_zh.md) 已经提供了图数据库引擎的能力。本文将详细介绍如何借助大模型以及 openGauss 图数据库快速提取文档知识图谱并持久化。 随后，将问题输入大模型，大模型自动提取关键词并转换为图查询语句连接 openGauss 图数据库进行图数据检索。最后，将图数据输入大模型生成问题答案反馈给用户。 +![AGEGraph](./figures/openGauss-AGEGraph.png) + +### 1.环境准备 + +#### 1.1 openGauss 容器化部署 +可参考[openGauss容器镜像安装](https://docs.opengauss.org/zh/docs/7.0.0-RC1/docs/InstallationGuide/%E5%AE%B9%E5%99%A8%E9%95%9C%E5%83%8F%E5%AE%89%E8%A3%85.html)部署openGauss。 + +openGauss容器启动后可以执行`docker ps`查看容器是否在运行 +输出 + +``` +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +253714c9c869 opengauss:7.0.0-rc1 "entrypoint.sh gauss…" 8 minutes ago Up 8 minutes 0.0.0.0:6543->5432/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 e27be8fd320168b70cdbdfdfa2816944f8a06e12..76bd9cfab7d1655b76fbd6556458bc3ac87c9e5b 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 fdf3cc46fc0a8a80687b324ef900b27759ab7af1..8932c25af747c33cf31b36e87c79f7846e95125e 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 Binary files /dev/null and b/content/zh/docs/DataVec/figures/openGauss-AGEGraph.png differ diff --git a/content/zh/docs/DataVec/openGauss-AGEGraph.md b/content/zh/docs/DataVec/openGauss-AGEGraph.md new file mode 100644 index 0000000000000000000000000000000000000000..a52f2c4663de820e6588859aff576a6e03d4369b --- /dev/null +++ b/content/zh/docs/DataVec/openGauss-AGEGraph.md @@ -0,0 +1,214 @@ +# openGauss AGEGraph + 大模型实现 GraphRAG，助力更强 RAG + +传统 RAG（检索增强生成）通常使用向量数据库检索相关文档来提高大模型的问题回答准确性，但是基于向量的 RAG 不能够很好的表达事物之间的联系。而GraphRAG基于知识图谱的检索增强生成，能够提供结构化检索能力，将图作为传统的RAG多路召回的一环，使知识表达更易解释、对复杂关系更易支持。 + +目前 [openGauss AGEGraph](https://gitee.com/opengauss/Plugin/blob/master/contrib/age/README_zh.md) 已经提供了图数据库引擎的能力。本文将详细介绍如何借助大模型以及 openGauss 图数据库快速提取文档知识图谱并持久化。 随后，将问题输入大模型，大模型自动提取关键词并转换为图查询语句连接 openGauss 图数据库进行图数据检索。最后，将图数据输入大模型生成问题答案反馈给用户。 +![AGEGraph](./figures/openGauss-AGEGraph.png) + +### 1.环境准备 + +#### 1.1 openGauss 容器化部署 +可参考[openGauss容器镜像安装](https://docs.opengauss.org/zh/docs/7.0.0-RC1/docs/InstallationGuide/%E5%AE%B9%E5%99%A8%E9%95%9C%E5%83%8F%E5%AE%89%E8%A3%85.html)部署openGauss。 + +openGauss容器启动后可以执行`docker ps`查看容器是否在运行 +输出 + +``` +CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES +253714c9c869 opengauss:7.0.0-rc1 "entrypoint.sh gauss…" 8 minutes ago Up 8 minutes 0.0.0.0:6543->5432/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/zh/menu/index.md b/content/zh/menu/index.md index 8ae385e31c0f4a27068aee83f70a295434d540ce..398ffd4688029a2c8a94d1e5b06830ff8f3bb34b 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" >}})