当前位置： 首页 > news >正文

网站图片幻灯片效果,万网域名查询接口,app定制开发服务商,wordpress主题太难看了GitHub代码仓库 架构 从高层次来看#xff0c;这些系统的步骤如下#xff1a; 将问题转换为SQL查询#xff1a;模型将用户输入转换为SQL查询。 执行SQL查询#xff1a;执行查询。 回答问题#xff1a;模型根据查询结果响应用户输入。 样本数据 下载样本数据#xf…GitHub代码仓库 架构 从高层次来看这些系统的步骤如下 将问题转换为SQL查询模型将用户输入转换为SQL查询。 执行SQL查询执行查询。 回答问题模型根据查询结果响应用户输入。 样本数据 下载样本数据 curl -s https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql | sqlite3 Chinook.db现在Chinook.db 位于我们的目录中我们可以使用 SQLAlchemy 驱动的 SQLDatabase 类与它进行交互 from langchain_community.utilities import SQLDatabasedb SQLDatabase.from_uri(sqlite:Documents/learn-langchain/example-data/Chinook.db) print(db.dialect) print(db.get_usable_table_names()) db.run(SELECT * FROM Artist LIMIT 10;)链 链条是可预测步骤的组合。在 LangGraph 中我们可以通过简单的节点序列来表示链条。让我们创建一个步骤序列给定一个问题执行以下操作 将问题转换为 SQL 查询 执行查询 使用结果回答原始问题。
这个安排并不支持所有场景。例如系统会对任何用户输入执行 SQL 查询——即使是“你好”。值得注意的是正如我们下面将看到的有些问题需要多次查询才能回答。我们将在“代理”部分解决这些场景。 应用状态 我们应用的 LangGraph 状态控制着输入到应用程序的数据、在步骤之间传递的数据以及应用程序输出的数据。它通常是一个 TypedDict也可以是一个 Pydantic BaseModel。 对于这个应用我们可以只跟踪输入的问题、生成的查询、查询结果和生成的答案 from typing_extensions import TypedDictclass State(TypedDict):question: strquery: strresult: stranswer: str现在我们只需要一些函数来操作这个状态并填充其内容。 将问题转换为 SQL 查询 第一步是将用户输入转换为 SQL 查询。为了可靠地获取 SQL 查询不包括 Markdown 格式的说明或解释我们将利用 LangChain 的结构化输出抽象。 from config import * from langchain_openai import ChatOpenAIllm ChatOpenAI(modeldeepseek-v3)我们将从 Prompt Hub 中获取一个提示来指导模型。 from langchain import hubquery_prompt_template hub.pull(langchain-ai/sql-query-system-prompt)assert len(query_prompt_template.messages) 1 query_prompt_template.messages[0].pretty_print()[1m System Message [0mGiven an input question, create a syntactically correct [33;1m[1;3m{dialect}[0m query to run to help find the answer. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most [33;1m[1;3m{top_k}[0m results. You can order the results by a relevant column to return the most interesting examples in the database.Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.Only use the following tables: [33;1m[1;3m{table_info}[0mQuestion: [33;1m[1;3m{input}[0m这个提示包含了我们需要填充的几个参数例如 SQL 方言和表模式。LangChain 的 SQLDatabase 对象包含了一些方法来帮助我们处理这些。我们的 write_query 步骤将只填充这些参数并提示模型生成 SQL 查询 from pydantic import BaseModel from typing_extensions import Annotatedfrom langchain_core.messages import SystemMessage from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.output_parsers import PydanticOutputParserclass QueryOutput(BaseModel):Generated SQL query.query: Annotated[str, …, Syntactically valid SQL query.]parser PydanticOutputParser(pydantic_objectQueryOutput)query_prompt ChatPromptTemplate.from_messages([(system, {format_instructions}),query_prompt_template] ).partial(format_instructionsparser.get_format_instructions())def write_query(state: State):Generate SQL query to fetch information.prompt query_prompt.invoke({dialect: db.dialect,top_k: 10,table_info: db.get_table_info(),input: state[question],})chain llm | parserresult chain.invoke(prompt)return {query: result.query}看一下query_prompt的内容 for message in query_prompt.messages:message.pretty_print()[1m System Message [0m[33;1m[1;3m{format_instructions}[0m [1m System Message [0mGiven an input question, create a syntactically correct [33;1m[1;3m{dialect}[0m query to run to help find the answer. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most [33;1m[1;3m{top_k}[0m results. You can order the results by a relevant column to return the most interesting examples in the database.Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.Only use the following tables: [33;1m[1;3m{table_info}[0mQuestion: [33;1m[1;3m{input}[0m让我们测试一下 write_query({question: How many Employees are there?}){query: SELECT COUNT() AS EmployeeCount FROM Employee;}执行查询 这是创建 SQL 链条中最危险的部分。在自动执行查询之前请仔细考虑是否可以对数据运行自动化查询。尽可能减少数据库连接权限。考虑在查询执行之前在链条中添加人工批准步骤见下文。 为了执行查询我们将从 langchain-community 加载一个工具。我们的 execute_query 节点只是封装这个工具 from langchain_community.tools.sql_database.tool import QuerySQLDatabaseTooldef execute_query(state: State):Execute SQL query.execute_query_tool QuerySQLDatabaseTool(dbdb)return {result: execute_query_tool.invoke(state[query])}测试一下 execute_query({query: SELECT COUNT() AS EmployeeCount FROM Employee;}){result: [(8,)]}生成答案 最后我们的最后一步是根据从数据库中提取的信息生成问题的答案 def generate_answer(state: State):Answer question using retrieved information as context.prompt (Given the following user question, corresponding SQL query, and SQL result, answer the user question.\n\nfQuestion: {state[question]}\nfSQL Query: {state[query]}\nfSQL Result: {state[result]})response llm.invoke(prompt)return {answer: response.content}使用 LangGraph 进行协调 最后我们将应用程序编译成一个单一的图形对象。在这种情况下我们只是将这三步连接成一个单一的序列。 from langgraph.graph import START, StateGraphgraph_builder StateGraph(State).add_sequence([write_query, execute_query, generate_answer] ) graph_builder.add_edge(START, write_query) graph graph_builder.compile()from IPython.display import Image, displaydisplay(Image(graph.get_graph().draw_mermaid_png()))测试一下应用 for step in graph.stream({question: How many employees are there?}, stream_modeupdates ):print(step){write_query: {query: SELECT COUNT(*) AS NumberOfEmployees FROM Employee;}} {execute_query: {result: [(8,)]}} {generate_answer: {answer: There are 8 employees in total.}}人工参与 LangGraph 支持许多对这个工作流有用的功能其中之一就是人工参与我们可以在敏感步骤如执行 SQL 查询之前中断应用程序以便进行人工审核。这是通过 LangGraph 的持久化层实现的该层将运行进度保存到您选择的存储中。下面我们指定了内存存储 from langgraph.checkpoint.memory import MemorySavermemory MemorySaver() graph graph_builder.compile(checkpointermemory, interrupt_before[execute_query])# Now that were using persistence, we need to specify a thread ID

so that we can continue the run after review.

config {configurable: {thread_id: 1}}display(Image(graph.get_graph().draw_mermaid_png()))让我们重复相同的运行并添加一个简单的 yes/no 审批步骤 for step in graph.stream({question: How many employees are there?},config,stream_modeupdates, ):print(step)try:user_approval input(Do you want to go to execute query? (yes/no): ) except Exception:user_approval noif user_approval.lower() yes:# If approved, continue the graph executionfor step in graph.stream(None, config, stream_modeupdates):print(step) else:print(Operation cancelled by user.){write_query: {query: SELECT COUNT(*) AS EmployeeCount FROM Employee;}} {interrupt: ()} {execute_query: {result: [(8,)]}} {generate_answer: {answer: There are 8 employees in total.}}代理 代理利用大型语言模型LLM的推理能力在执行过程中做出决策。使用代理可以将更多的判断权转移到查询生成和执行过程中。尽管它们的行为比上述“链条”更不可预测但它们也有一些优势 • 它们可以根据需要多次查询数据库以回答用户问题。 • 它们可以通过运行生成的查询捕获回溯并正确地重新生成查询从而从错误中恢复。 • 它们不仅可以根据数据库的内容回答问题还可以基于数据库的模式回答问题比如描述特定的表。 下面我们组装一个最小的 SQL 代理。 from langchain_community.agent_toolkits import SQLDatabaseToolkittoolkit SQLDatabaseToolkit(dbdb, llmllm)tools toolkit.get_tools()tools[QuerySQLDatabaseTool(descriptionInput to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column xxxx in field list, use sql_db_schema to query the correct table fields., dblangchain_community.utilities.sql_database.SQLDatabase object at 0x11919bee0),InfoSQLDatabaseTool(descriptionInput to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3, dblangchain_community.utilities.sql_database.SQLDatabase object at 0x11919bee0),ListSQLDatabaseTool(dblangchain_community.utilities.sql_database.SQLDatabase object at 0x11919bee0),QuerySQLCheckerTool(descriptionUse this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!, dblangchain_community.utilities.sql_database.SQLDatabase object at 0x11919bee0, llmChatOpenAI(clientopenai.resources.chat.completions.Completions object at 0x11d442b80, async_clientopenai.resources.chat.completions.AsyncCompletions object at 0x11d4841f0, root_clientopenai.OpenAI object at 0x11d3e05b0, root_async_clientopenai.AsyncOpenAI object at 0x11d442bb0, model_namedeepseek-v3, model_kwargs{}, openai_api_keySecretStr(*******), openai_api_basehttps://dashscope.aliyuncs.com/compatible-mode/v1), llm_chainLLMChain(verboseFalse, promptPromptTemplate(input_variables[dialect, query], input_types{}, partial_variables{}, template\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n\nOutput the final SQL query only.\n\nSQL Query: ), llmChatOpenAI(clientopenai.resources.chat.completions.Completions object at 0x11d442b80, async_clientopenai.resources.chat.completions.AsyncCompletions object at 0x11d4841f0, root_clientopenai.OpenAI object at 0x11d3e05b0, root_async_clientopenai.AsyncOpenAI object at 0x11d442bb0, model_namedeepseek-v3, model_kwargs{}, openai_api_keySecretStr(*******), openai_api_basehttps://dashscope.aliyuncs.com/compatible-mode/v1), output_parserStrOutputParser(), llm_kwargs{}))]系统提示 我们还需要为我们的代理加载一个系统提示。这将包括行为指令。 from langchain import hubprompt_template hub.pull(langchain-ai/sql-agent-system-prompt)assert len(prompt_template.messages) 1让我们填充提示中的参数 system_message prompt_template.format(dialectSQLite, top_k5) print(system_message)System: You are an agent designed to interact with a SQL database. Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return the answer. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results. You can order the results by a relevant column to return the most interesting examples in the database. Never query for all the columns from a specific table, only ask for the relevant columns given the question. You have access to tools for interacting with the database. Only use the below tools. Only use the information returned by the below tools to construct your final answer. You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.To start you should ALWAYS look at the tables in the database to see what you can query. Do NOT skip this step. Then you should query the schema of the most relevant tables.初始化代理 我们将使用一个预构建的 LangGraph 代理来构建我们的代理。 from langchain_core.messages import HumanMessage from langgraph.prebuilt import create_react_agentllm ChatOpenAI(modelqwen-max) agent_executor create_react_agent(llm, tools, promptsystem_message)display(Image(agent_executor.get_graph().draw_mermaid_png()))question Which countrys customers spent the most?result agent_executor.invoke({messages: [{role: user, content: question}]})for m in result[messages]:m.pretty_print()[1m Human Message [0mWhich countrys customers spent the most? [1m Ai Message [0m Tool Calls:sql_db_list_tables (call_11959469bb4c42ab8faaee)Call ID: call_11959469bb4c42ab8faaeeArgs:tool_input: [1m Tool Message [0m Name: sql_db_list_tablesAlbum, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track [1m Ai Message [0m Tool Calls:sql_db_schema (call_6f549cbdefa94a5e80152e)Call ID: call_6f549cbdefa94a5e80152eArgs:table_names: Customer,Invoice [1m Tool Message [0m Name: sql_db_schemaCREATE TABLE Customer (CustomerId INTEGER NOT NULL, FirstName NVARCHAR(40) NOT NULL, LastName NVARCHAR(20) NOT NULL, Company NVARCHAR(80), Address NVARCHAR(70), City NVARCHAR(40), State NVARCHAR(40), Country NVARCHAR(40), PostalCode NVARCHAR(10), Phone NVARCHAR(24), Fax NVARCHAR(24), Email NVARCHAR(60) NOT NULL, SupportRepId INTEGER, PRIMARY KEY (CustomerId), FOREIGN KEY(SupportRepId) REFERENCES Employee (EmployeeId) )/* 3 rows from Customer table: CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId 1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 55 (12) 3923-5555 55 (12) 3923-5566 luisgembraer.com.br 3 2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 49 0711 2842222 None leonekohlersurfeu.de 5 3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 1 (514) 721-4711 None ftremblaygmail.com 3 /CREATE TABLE Invoice (InvoiceId INTEGER NOT NULL, CustomerId INTEGER NOT NULL, InvoiceDate DATETIME NOT NULL, BillingAddress NVARCHAR(70), BillingCity NVARCHAR(40), BillingState NVARCHAR(40), BillingCountry NVARCHAR(40), BillingPostalCode NVARCHAR(10), Total NUMERIC(10, 2) NOT NULL, PRIMARY KEY (InvoiceId), FOREIGN KEY(CustomerId) REFERENCES Customer (CustomerId) )/ 3 rows from Invoice table: InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total 1 2 2021-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98 2 4 2021-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96 3 8 2021-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94 */ [1m Ai Message [0m Tool Calls:sql_db_query_checker (call_a859a5ed305e42d2b9048f)Call ID: call_a859a5ed305e42d2b9048fArgs:query: SELECT Customer.Country, SUM(Invoice.Total) AS TotalSpent FROM Invoice JOIN Customer ON Invoice.CustomerId Customer.CustomerId GROUP BY Customer.Country ORDER BY TotalSpent DESC LIMIT 5; [1m Tool Message [0m Name: sql_db_query_checkersql SELECT Customer.Country, SUM(Invoice.Total) AS TotalSpent FROM Invoice JOIN Customer ON Invoice.CustomerId Customer.CustomerId GROUP BY Customer.Country ORDER BY TotalSpent DESC LIMIT 5;[1m Ai Message [0m Tool Calls:sql_db_query (call_5b569709098b49dd9336e5)Call ID: call_5b569709098b49dd9336e5Args:query: SELECT Customer.Country, SUM(Invoice.Total) AS TotalSpent FROM Invoice JOIN Customer ON Invoice.CustomerId Customer.CustomerId GROUP BY Customer.Country ORDER BY TotalSpent DESC LIMIT 5; [1m Tool Message [0m Name: sql_db_query[(USA, 523.06), (Canada, 303.96), (France, 195.1), (Brazil, 190.1), (Germany, 156.48)] [1m Ai Message [0mThe countries whose customers spent the most, in descending order, are as follows:1. USA - $523.06

1. Canada - $303.96
2. France - $195.10
3. Brazil - $190.10
4. Germany - $156.48These are the top 5 countries with the highest spending customers.代理会执行多个查询直到获取所需的信息 1. 列出可用的表 2. 获取三个表的模式 3. 通过联接操作查询多个表。 然后代理能够使用最终查询的结果来生成原始问题的答案。 代理同样可以处理定性问题 question Describe the playlisttrack tableresult agent_executor.invoke({messages: [{role: user, content: question}]})for m in result[messages]:m.pretty_print()[1m Human Message [0mDescribe the playlisttrack table [1m Ai Message [0m Tool Calls:sql_db_schema (call_b9ead21107664c6a851c85)Call ID: call_b9ead21107664c6a851c85Args:table_names: playlisttrack [1m Tool Message [0m Name: sql_db_schemaError: table_names {playlisttrack} not found in database [1m Ai Message [0mIt seems that there was an error because the table playlisttrack could not be found in the database. Let me first retrieve a list of the available tables to check if the correct table name is being used. Tool Calls:sql_db_list_tables (call_fc6f21ca229c4a84adb266)Call ID: call_fc6f21ca229c4a84adb266Args:tool_input: [1m Tool Message [0m Name: sql_db_list_tablesAlbum, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track [1m Ai Message [0mThe correct table name is indeed PlaylistTrack, and it is available in the database. Let me now retrieve the schema for the PlaylistTrack table. Tool Calls:sql_db_schema (call_ee9bcfe4dfae45edb01325)Call ID: call_ee9bcfe4dfae45edb01325Args:table_names: PlaylistTrack [1m Tool Message [0m Name: sql_db_schemaCREATE TABLE PlaylistTrack (PlaylistId INTEGER NOT NULL, TrackId INTEGER NOT NULL, PRIMARY KEY (PlaylistId, TrackId), FOREIGN KEY(TrackId) REFERENCES Track (TrackId), FOREIGN KEY(PlaylistId) REFERENCES Playlist (PlaylistId) )/* 3 rows from PlaylistTrack table: PlaylistId TrackId 1 3402 1 3389 1 3390 */ [1m Ai Message [0mThe PlaylistTrack table has the following schema:- PlaylistId (INTEGER, NOT NULL): This is a foreign key that references the Playlist table and is part of the composite primary key.

• TrackId (INTEGER, NOT NULL): This is a foreign key that references the Track table and is also part of the composite primary key.The primary key for this table is a combination of PlaylistId and TrackId, which means that each track can only appear once in a given playlist. Here are a few sample rows from the PlaylistTrack table to illustrate:| PlaylistId | TrackId | |————|———| | 1 | 3402 | | 1 | 3389 | | 1 | 3390 |If you need more specific information or a query based on this table, please let me know!