> ## Documentation Index > Fetch the complete documentation index at: https://langchain-docs-zh.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # 使用 HanaSparqlQAAgent 进行问答 > 使用 LangChain Python 集成 HanaSparqlQAAgent 类型 ## 设置与安装 要使用此功能，请安装 `langchain-hana` 包： ```python theme={null} pip install langchain_hana ``` `HanaSparqlQAAgent` 是一种基于智能体的方法，用于回答存储在 SAP HANA Cloud 中的 RDF 数据的问题。与基于链的方法不同，智能体可以： 1. **动态检索本体** 使用专用工具 2. **生成并执行 SPARQL 查询** 迭代 3. **自我纠正** 如果查询失败或返回意外结果 4. **逐步推理** 关于复杂问题 ## 初始化 您需要： * 一个 **LLM** 为智能体的推理提供支持 * 一个 **`HanaRdfGraph`**（包含连接、`graph_uri` 和本体） 请按照此处 [HanaRdfGraph](https://langchain-docs-zh.mintlify.site/langchain/oss/python/integrations/graphs/sap_hana_rdf_graph.md) 的步骤了解更多关于创建 `HanaRdfGraph` 实例的信息。 导入 `HanaSparqlQAAgent` ```python theme={null} from langchain_hana import HanaSparqlQAAgent agent = HanaSparqlQAAgent.create_agent(graph=graph, model=llm) ``` ## 智能体概述 智能体使用工具迭代执行以下操作： 1. 首先检索本体以理解数据结构 2. 根据模式生成适当的 SPARQL 查询 3. 执行查询并解释结果 4. 制定自然语言答案 ### 默认设置 * **工具**: * `retrieve_ontology` - 从图中以 Turtle 格式检索 RDF 本体/模式 * `execute_sparql` - 对 HANA RDF 图执行 SPARQL 查询 * **系统提示词**：提供默认系统提示词，包含 SPARQL 生成和工具使用的说明。 * **中间件**：`ModelRetryMiddleware(max_retries=3)` 和 `ToolRetryMiddleWare(max_retries=2)` 默认添加以防止无限循环 ### 自定义您的智能体 您可以通过向 `create_agent` 提供附加参数来自定义智能体的行为： ```python theme={null} agent = HanaSparqlQAAgent.create_agent( graph=graph, # Required: HanaRdfGraph instance model=llm, # Required: LLM to power the agent system_prompt=your_custom_prompt, # Custom system prompt (str or SystemMessage) tools=[your_custom_tools], # Additional tools to include middleware=[your_custom_middlewares], # Additional middleware to include include_default_tools=True, # include default tools (default: True) include_default_middleware=True, # include default middleware (default: True) ) ``` ## 示例：在 "Movies" 知识图谱上进行问答 **前提条件**： 您必须有一个启用了 **三元组存储** 功能的 SAP HANA Cloud 实例。 有关详细说明，请参阅：[启用三元组存储](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-knowledge-graph-guide/enable-triple-store/)\ 加载 `kgdocu_movies` 示例数据。参见 [知识图谱示例](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-knowledge-graph-guide/knowledge-graph-example)。 以下示例： 1. 实例化 `HanaRdfGraph` 指向电影数据图 2. 创建一个由 LLM 驱动的 `HanaSparqlQAAgent` 3. 提问自然语言问题，并让智能体通过推理得出答案 这展示了智能体如何动态检索本体、生成 SPARQL 查询并返回人类可读的答案。 ```python theme={null} import os from dotenv import load_dotenv from hdbcli import dbapi # Load environment variables if needed load_dotenv() # Establish connection to SAP HANA Cloud connection = dbapi.connect( address=os.environ.get("HANA_DB_ADDRESS"), port=os.environ.get("HANA_DB_PORT"), user=os.environ.get("HANA_DB_USER"), password=os.environ.get("HANA_DB_PASSWORD") ) ``` 然后，设置知识图谱实例 ```python theme={null} from gen_ai_hub.proxy.langchain.openai import ChatOpenAI from langchain_hana import HanaRdfGraph, HanaSparqlQAChain # from langchain_openai import ChatOpenAI # or your chosen LLM ``` ```python theme={null} # Set up the Knowledge Graph graph_uri = "kgdocu_movies" graph = HanaRdfGraph( connection=connection, graph_uri=graph_uri, auto_extract_ontology=True ) ``` ```python theme={null} # a basic graph schema is extracted from the data graph. This schema will guide the LLM to generate a proper SPARQL query. schema_graph = graph.get_schema print(schema_graph.serialize(format="turtle")) ``` ```output theme={null} @prefix owl: . @prefix rdfs: . @prefix xsd: . a owl:ObjectProperty ; rdfs:label "acted_in" ; rdfs:domain ; rdfs:range . a owl:DatatypeProperty ; rdfs:label "dateOfBirth" ; rdfs:domain ; rdfs:range xsd:dateTime . a owl:ObjectProperty ; rdfs:label "directed" ; rdfs:domain ; rdfs:range . a owl:ObjectProperty ; rdfs:label "genre" ; rdfs:domain ; rdfs:range . a owl:ObjectProperty ; rdfs:label "placeOfBirth" ; rdfs:domain ; rdfs:range . a owl:DatatypeProperty ; rdfs:label "title" ; rdfs:domain ; rdfs:range xsd:string . rdfs:label a owl:DatatypeProperty ; rdfs:label "label" ; rdfs:domain , , , ; rdfs:range xsd:string . a owl:Class ; rdfs:label "Director" . a owl:Class ; rdfs:label "Genre" . a owl:Class ; rdfs:label "Place" . a owl:Class ; rdfs:label "Actor" . a owl:Class ; rdfs:label "Film" . ``` 之后，初始化 LLM。 ```python theme={null} # Initialize the LLM llm = ChatOpenAI(proxy_model_name="gpt-4o", temperature=0) ``` 然后，创建 SPARQL 问答智能体。 ```python theme={null} # Create a SPARQL QA Agent agent = HanaSparqlQAAgent.create_agent( graph=graph, model=llm, ) ``` ```python theme={null} # Example queries: # question = "Which movies are in the data?" # question = "In which movies did Keanu Reeves and Carrie-Anne Moss play together" # question = "which movie genres are in the data?" # question = "which are the two most assigned movie genres?" # question = "where were the actors of 'Blade Runner' born?" # question = "which actors acted together in a movie and were born in the same city?" question = "which actors acted in Blade Runner?" for event in agent.stream( {"messages": [{"role": "user", "content": question}]}, stream_mode="values", ): event["messages"][-1].pretty_print() ``` ```output theme={null} ================================ Human Message ================================= which actors acted in Blade Runner? ================================== Ai Message ================================== Tool Calls: retrieve_ontology (call_9T3Dxy3zKczTQKVAgTxPBfQm) Call ID: call_9T3Dxy3zKczTQKVAgTxPBfQm Args: ================================== Ai Message ================================== Tool Calls: retrieve_ontology (call_9T3Dxy3zKczTQKVAgTxPBfQm) Call ID: call_9T3Dxy3zKczTQKVAgTxPBfQm Args: ================================= Tool Message ================================= Name: retrieve_ontology Ontology Information: @prefix owl: . @prefix rdfs: . @prefix xsd: . a owl:ObjectProperty ; rdfs:label "acted_in" ; rdfs:domain ; rdfs:range . a owl:DatatypeProperty ; rdfs:label "dateOfBirth" ; rdfs:domain ; rdfs:range xsd:dateTime . a owl:ObjectProperty ; rdfs:label "directed" ; rdfs:domain ; rdfs:range . a owl:ObjectProperty ; rdfs:label "genre" ; rdfs:domain ; rdfs:range . a owl:ObjectProperty ; rdfs:label "placeOfBirth" ; rdfs:domain ; rdfs:range . a owl:DatatypeProperty ; rdfs:label "title" ; rdfs:domain ; rdfs:range xsd:string . rdfs:label a owl:DatatypeProperty ; rdfs:label "label" ; rdfs:domain , , , ; rdfs:range xsd:string . a owl:Class ; rdfs:label "Director" . a owl:Class ; rdfs:label "Genre" . a owl:Class ; rdfs:label "Place" . a owl:Class ; rdfs:label "Actor" . a owl:Class ; rdfs:label "Film" . ================================== Ai Message ================================== Tool Calls: execute_sparql (call_LwHuDXaPsx3nDJF7Hn6If8s9) Call ID: call_LwHuDXaPsx3nDJF7Hn6If8s9 Args: query: PREFIX kg: PREFIX rdf: PREFIX rdfs: SELECT ?actorLabel FROM WHERE { ?movie rdf:type kg:Film . ?movie kg:title "Blade Runner" . ?actor kg:acted_in ?movie . ?actor rdfs:label ?actorLabel . } ================================== Ai Message ================================== Tool Calls: execute_sparql (call_LwHuDXaPsx3nDJF7Hn6If8s9) Call ID: call_LwHuDXaPsx3nDJF7Hn6If8s9 Args: query: PREFIX kg: PREFIX rdf: PREFIX rdfs: SELECT ?actorLabel FROM WHERE { ?movie rdf:type kg:Film . ?movie kg:title "Blade Runner" . ?actor kg:acted_in ?movie . ?actor rdfs:label ?actorLabel . } ================================= Tool Message ================================= Name: execute_sparql SPARQL Query Result: actorLabel Morgan Paull William Sanderson James Hong M. Emmet Walsh Joe Turkel Brion James Q81328 Daryl Hannah Rutger Hauer Joanna Cassidy Hy Pyke Sean Young Edward James Olmos ================================== Ai Message ================================== The actors who acted in "Blade Runner" are: - Morgan Paull - William Sanderson - James Hong - M. Emmet Walsh - Joe Turkel - Brion James - Daryl Hannah - Rutger Hauer - Joanna Cassidy - Hy Pyke - Sean Young - Edward James Olmos ================================== Ai Message ================================== The actors who acted in "Blade Runner" are: - Morgan Paull - William Sanderson - James Hong - M. Emmet Walsh - Joe Turkel - Brion James - Daryl Hannah - Rutger Hauer - Joanna Cassidy - Hy Pyke - Sean Young - Edward James Olmos ``` ### 底层工作原理是什么？ 1. **本体检索** 智能体首先使用 `retrieve_ontology` 工具以 Turtle 格式获取 RDF 模式。这使其了解可用的类、属性和关系。 2. **SPARQL 生成** 根据本体和用户的问题，智能体推理要查询的实体和属性，然后生成一个有效的 `SELECT` 查询。 3. **查询执行** 智能体调用 `execute_sparql` 工具在 HANA RDF 图上运行生成的查询。如果查询失败或返回意外结果，智能体可以自我纠正并重试。 4. **答案制定** 智能体解释查询结果，并严格基于检索到的数据制定简洁、人类可读的答案。 *** > 官方原文：[src/oss/python/integrations/agents/sap\_hana\_sparql\_qa\_agent.mdx](https://github.com/langchain-ai/docs/blob/main/src/oss/python/integrations/agents/sap_hana_sparql_qa_agent.mdx) · 非官方翻译，译文由 AI 生成，以英文原文为准。 This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.