Langchain js sql agent \nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. 这个示例展示了如何加载和使用SQL工具包中的代理。 import {OpenAI } from "langchain/llms/openai"; import {SqlDatabase } from "langchain/sql_db"; import {createSqlAgent, SqlToolkit } from "langchain/agents"; import {DataSource } from "typeorm"; In simple terms, langchain is a framework and library of useful templates and tools that make it easier to build large language model applications that use custom data and external tools. At a high level, the agent will: 1. Transition Steps. Example const model = new ChatOpenAI ({}); const toolkit = new SqlToolkit ( sqlDb , model ); const executor = createSqlAgent ( model , toolkit ); const result = await executor . meta. It uses Mistral-7b via llama. db; Run . This example demonstrates the use of Runnables with questions and more on a SQL database. How to better prompt when doing SQL question-answering; In LangGraph, the graph replaces LangChain's agent executor. Now, we can evaluate this agent! We previously defined simple SQL agent as part of our LangSmith evaluation cookbooks, and evaluated responses to 5 questions about our database. In this tutorial, we will walk through how to build an agent that can answer questions about a SQL database. SQL_ SUFFIX: "Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query. This will help you getting started with the SQL Database toolkit. import * as yaml from "js-yaml"; import {OpenAI } from "@langchain/openai"; import import {SQL_PROMPTS_MAP} from "langchain/chains/sql_db"; console. ” It initializes SQL tools based on the provided SQL database. This is documentation for LangChain v0. I'm integrating a SQL agent with LangChain in a Node. Documentation for LangChain. sql; Run sqlite3 Chinook. The output is the agent response. invoke("总共有多少用 构建 Agent. LangChain comes with a number of built-in agents that are optimized for different use cases. View the latest docs here. js（TypeScript） version 0. Params required to create the agent. js; langchain; tools/sql; InfoSqlTool; Class InfoSqlTool. js This example shows how to load and use an agent with a JSON toolkit. It uses LLamA2-13b hosted by Replicate, but can be adapted to any API that supports LLaMA2 including Fireworks. Deprecated This project integrates LangChain with a MySQL database to enable conversational interactions with the database. A common application is to enable agents to answer questions using data in a relational database, Returns Promise < AgentRunnableSequence < { steps: AgentStep []; }, AgentAction | AgentFinish > >. 37 Documentation for LangChain. tsx and action. LangChain提供与SQL数据库交互的工具： * 根据自然语言用户问题构建SQL查询 * 使用链式查询创建和执行SQL数据库查询 * 使用代理与SQL数据库交互，实现强大灵活的查询 企业数据通常存储在SQL数据库中。 LLM使得可以使用自然语言与SQL数据库进行交互。 How to stream agent data to the client. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. keys (SQL_PROMPTS 3 Peacock Jane Sales Support Agent 2 1973-08-29 00:00:00 2002-04-01 00:00:00 1111 6 Ave SW Calgary AB Canada T2P 5M5 +1 (403) 262-3443 +1 (403) 262-6712 jane@chinookcorp. 这个示例展示了如何加载和使用SQL工具包中的代理。 import {OpenAI } from "langchain/llms/openai"; import {SqlDatabase } from "langchain/sql_db"; import {createSqlAgent, SqlToolkit } from "langchain/agents"; import {DataSource } from "typeorm"; Lots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables. By default, this does retrieval over Arxiv. Remarks. Now let's try hooking it up to an LLM. Save this file as Chinook_Sqlite. llama3-2-1b-instruct-v1:0) for natural language to SQL conversion. Convert question to SQL query The first step in a SQL chain or agent is to take the Stream all output from a runnable, as reported to the callback system. In LangGraph, we can represent a chain via simple sequence of nodes. utilities import SQLDatabase. Response: The inputs are a prompt and a list of tools. The post has a really helpful In this tutorial, we will walk through step-by-step, the creation of a LangChain enabled, large language model (LLM) driven, agent that can use a SQL database to answer questions. Security Notice This class generates SQL queries for the given database. This notebook showcases an agent designed to interact with a SQL databases. Newer LangChain version out! You are currently viewing the old v0. Community. log ({SQL_PROMPTS_MAP: Object. js application using the AWS Bedrock model (us. In practice, this LangChain系列文章 1. Database: PostgreSQL When integrating a SQL agent with LangChain to convert natural language queries into SQL using an AI model like the one from AWS Bedrock, Stream all output from a runnable, as reported to the callback system. Chains . \n{agent_scratchpad}" = I'm integrating a SQL agent with LangChain in a Node. For the current stable version, see this version SQL Agent Toolkit. 3. いまさらながら、Langchainを用いて、自然言語でMySQLを操作してみる。 果たしてモデルの向上で使えるレベルになっているだろうか。 使うもの. The agent is responsible for taking in input and deciding what actions to take. com import {examples } from Documentation for LangChain. spark_sql import SparkSQL from langchain_openai import ChatOpenAI. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in SQLDatabase Toolkit. Like Autonomous Agents, Agent Simulations are still experimental and based on papers such as this one. To set up the environment, use the following steps: One of the common types of databases that we can build Q&A systems for are graph databases. LangChain comes with a number of built-in chains and agents that are compatible with graph query language dialects like Cypher, Neo4j, Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. invoke ({ input: 'List the total sales per country. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Stream all output from a runnable, as reported to the callback system. The code in this doc is taken from the page. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs Documentation for LangChain. This app will generate SQL SQL_PREFIX:"You are an agent designed to interact with a SQL database. A tool for retrieving information about SQL tables. Tools within the SQLDatabaseToolkit are designed to interact with a SQL database. 1 docs. Fetch the available tables from the database 2. 5k次，点赞22次，收藏25次。可以自定义所使用的prompt提示模板，这是使用官方的一个prompt示例# 导入langchain的实用工具和相关的模块# 连接到demo数据库# 创建LLM# 创建一个生成 SQL 查询的链# 运行查询问题response = db_chain. The SQLDatabase class provides a getTableInfo method that can be used to get column information as well as sample data from the table. The below example will use a SQLite connection with Chinook database. Agent Constructor Here, we will use the high level createOpenaiToolsAgent API to construct the agent. js Eval¶. read Chinook_Sqlite. sql; Test SELECT * FROM Artist LIMIT 10;; Now, Chinook. Chains are compositions of predictable steps. agent_toolkits import SQLDatabaseToolkit toolkit = SQLDatabaseToolkit(db=db, llm=llm) agent = create_sql_agent(llm=llm, toolkit=toolkit, The toolkit offers various tools which helps the agent to take actions. We can compare this agent to our prior one on the same dataset. The prompt in the LLMChain must include a variable called "agent_scratchpad" where the agent can put its intermediary work. Next. Deprecated sql-llamacpp. For more information on how to build from langchain_openai import ChatOpenAI from langchain_community. SQL. Agent evaluation can focus on 3 things:. Chat Memory. 语言模型本身无法执行操作 - 它们只是输出文本。 LangChain 的一个重要用例是创建 agents。Agents 是使用 LLM 作为推理引擎的系统，以确定要采取哪些操作以及执行操作所需的输入。 执行操作后，可以将结果反馈回 LLM，以确定是否需要更多操作，或者是否可以完成。 This code demo's how you can connect to an SQL database using langchain SQL agent, query the data with natural language and send it to the LLM for generating a insightful response About Langchain SQL agent example to talk to The below example will use a SQLite connection with Chinook database. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Here’s a simple example of how to create a SQL Agent that interacts with a PostgreSQL database: from langchain. \nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the The LangChain library has multiple SQL chains and even an SQL agent aimed at making interacting with data stored in SQL as easy as possible. LangChain Hub; LangChain JS/TS; v0. We recommend that you use LangGraph for building agents. It takes a SQL database as a parameter and assigns it to the db property. For detailed documentation of all SQLDatabaseToolkit features and configurations head to the API reference. 1. js. Skip to main content. It manages the agent's cycles and tracks the scratchpad as messages within its state. These tools will be visible below when Documentation for LangChain. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). Let's create a sequence of steps that, given a LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. Please see the following resources for more information: LangGraph docs on common agent architectures; Pre-built agents in LangGraph; Legacy agent concept: AgentExecutor It initializes SQL tools based on the provided SQL database. 请注意，由于该代理正在积极开发中，所有答案可能不正确。 sql-llama2. db is in our directory and we can interface with it using the Documentation for LangChain. This example demonstrates the use of the SQLDatabaseChain for answering questions over a SQL database. Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so. This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. cpp to run inference locally on a Mac laptop. agent_toolkits import SparkSQLToolkit from langchain_community. sql; Test SELECT * FROM Artist LIMIT 10;; Now, Chinhook. Discord; Twitter; GitHub. SQL_ PREFIX: "You are an agent designed to interact with a SQL database. It is designed to answer more general questions about a database, as well as recover from errors. This page covers all resources available in LangChain for working with data in this format. This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. 📄️ Generative Agents. It takes as input all the same input variables as the prompt passed in does. js - v0. js List of tools the agent will have access to, used to format the prompt. db in the same directory as this notebook:. 17; Cloud SQL（Cloud SQL Auth Proxy） 結果的にはCloud SQL特有のエラーには遭遇しなかった。 Documentation for LangChain. 本笔记本展示了一个与sql数据库交互的代理。该代理基于SQLDatabaseChain构建，并旨在回答有关数据库的更一般的问题，并从错误中恢复。. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Stream all output from a runnable, as reported to the callback system. db is in our directory and we can interface with it using the LangChain Hub; JS/TS Docs; from langchain. LangChain JS/TS 中文文档 SQL Agent Toolkit. Follow these installation steps to create Chinook. The main advantages of using the SQL Agent are: It can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table). Environment Setup . The tools are: sql_db_query, sql_db_schema, sql_db_list_tables, sql_db_query_checker. It returns as output either an AgentAction or AgentFinish. ， 2. agents import create_sql_agent from langchain_community. js The below example will use a SQLite connection with Chinook database. Notice that beside the list of tools, the only thing we need to pass in is a language model to use. 我们将使用 langchain_community 包中提供的便捷 SQL 数据库包装器与数据库进行交互。 该包装器提供了一个简单的接口来执行 SQL 查询并获取结果。我们还将使用 langchain_openai 包在教程后面与 OpenAI API for language models 进行交互。 %% capture--no-stderr--no-display! pip install langgraph langchain_community langchain_openai Documentation for LangChain. 📄️ Violation of Expectations Chain Integration with Langchain SQL Agent JS: LangGraph supports integration with various components, including the langchain SQL agent in JavaScript, facilitating seamless data handling and processing. For more information about how to think about these components, see our conceptual guide. The main advantages of using This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. utilities. agents import create_spark_sql_agent from langchain_community. The LangChain "agent" corresponds to the prompt and LLM you've provided. \nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results LangChain JS/TS 中文文档 SQL Agent Toolkit. It returns as Great! We've got a SQL database that we can query. js Let's create a simple chain that takes a question, turns it into a SQL query, executes the query, and uses the result to answer the original question. LangChain Hub; JS/TS Docs; LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Setting this to true means that after the tool is called, an agent should stop looping. This script implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Park, et. db is in our directory and we can interface with it using the SQL数据库代理. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Documentation for LangChain. A User can have multiple Orders (one-to-many) A Product can be in multiple Orders (one-to-many) An Order belongs to one User and one Product (many-to-one for both, not unique) 文章浏览阅读2. To transition from AgentExecutor to Natural language querying allows users to interact with databases more intuitively and efficiently. Returns Promise < AgentRunnableSequence < { steps: ToolsAgentStep []; }, AgentFinish | AgentAction [] > >. Read about all the available agent types here. API Reference: create_spark_sql_agent; Figure 4:Pie chart with the network logs “Hi there! I’ve shared the network logs for you to analyze response behavior and latency. LangChain Hub; JS/TS Docs; solo-performance-prompting-agent; sql-llama2; sql-llamacpp; sql-ollama; sql-pgvector; sql-research-assistant; stepback-qa-prompting; The SQL Agent provided by LangChain is a tool that allows you to interact with SQL databases using natural language. Docs Use cases Integrations API Reference. Here are some relevant links: Most of an enterprise’s data is traditionally SQL. It is designed to be more flexible and more powerful than the standard SQLDatabaseChain, and it can be used to answer more general questions about a database, as well as recover from errors. SQL Database. agent_toolkits import create_sql_agent from langchain_community. Security. For this example, let’s try out the OpenAI tools agent, which makes use of the new OpenAI tool-calling API (this is only available in the latest OpenAI models, and differs from function-calling in that Documentation for LangChain. 1. 1, which is no longer actively maintained. Chains If you are just getting started, and you have relatively small/simple tabular data, you should get started with chains. Additionally, it integrates with Langsmith for tracing and feedback collection. al. In this post, we’ll walk you through creating a LangChain agent that can understand questions in natural language (NLP), dynamically generate SQL queries based on your input, fetch results We will explain how to implement an SQL Agent using LangChain, OpenAI API, and DuckDB, and how to turn it into an application with Morph. It leverages natural language processing (NLP) to query and manipulate database information using simple, conversational language. \n{agent_scratchpad}" = In this tutorial, we will walk through step-by-step, the creation of a LangChain enabled, large language model (LLM) driven, agent that can use a SQL database to answer questions. Includes an LLM, tools, and prompt. Essentially, langchain makes it easier to build It initializes SQL tools based on the provided SQL database. LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. \n{agent_scratchpad}" = This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. SqlDatabase from langchain/sql_db We've seen how to dynamically include a subset of table schemas in a prompt within a chain. db is in our directory and we can interface with it using the This package uses open source models hosted on FireworksAI to do retrieval using an agent architecture. To view the full, uninterrupted code, click here for the actions file and here for the client file. Feel free to run your code and, if needed, contribute to the GitHub repository by forking it, making changes in your fork, and submitting a pull request to the original repository. ts files in this directory. A runnable sequence representing an agent. Preparing search index The search index is not available; LangChain. Setup This example uses Chinook database, which is a sample database We're really excited by their approach to combining agent-based methods, LLMs, and synthetic data to enable natural language queries for databases and data warehouses, sans SQL. Under the hood, this agent is using the OpenAI tool-calling capabilities, so we need to use a ChatOpenAI model. js LangChain. This includes all inner runs of LLMs, Retrievers, Tools, etc. Now, we can initalize the agent with the LLM, the prompt, and the tools. langchain. . 37 Stream all output from a runnable, as reported to the callback system. Note that, as this agent is in active development, all answers might not be correct. This template enables a user to interact with a SQL database using natural language. The template includes an example database of 2023 NBA rosters. This is driven by an LLMChain. Python; JS/TS; More. bxj ghtls ntd xtr reughyp lwf zrxe svzbuo qkav txdoq hxdapix mikf eepre yxn bragrbpz