Sql agent langchain.
You are an agent designed to interact with a SQL database.
Sql agent langchain. LangChain comes with a built-in chain for this: create_sql_query_chain. Dec 5, 2023 · In my previous blog, we explored the Langchain tool and its remarkable create_sql_agent function, which enables the creation of a powerful SQL Agent with just a few lines of code. Toolkit is created using ‘db’ and You are an agent designed to interact with a SQL database. Feb 19, 2024 · In this post, basic LangChain components (toolkits, chains, agents) will be used to create a natural language to SQL prompt that will allow interactions with an Azure SQL Database; just ask the database what you want as if speaking to another person. Then, we'll go through the three most effective types of evaluations to run on chat bots: Final response: Evaluate the agent's final response. I built an SQL Agent with Langchain - Here's my experience My agent writes queries to retrieve data from Sqlite Databases. In this article, I will show you how we can use LangChain Agent and Azure OpenAI gpt-35-turbo model to query your SQL database using natural language (without writing any SQL at all!) and get useful data insights. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. g. agent_toolkits. Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). . Dec 9, 2024 · Construct a SQL agent from an LLM and toolkit or database. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. Mar 10, 2025 · We will explain how to implement an SQL Agent using LangChain, OpenAI API, and DuckDB , and how to Tagged with ai, openai, langchain, agenticai. , of tool calls) to arrive at the final answer. We will use a handy SQL database wrapper available in the langchain_community package to interact with the database. In this tutorial, we'll build a customer support bot that helps users navigate a digital music store. This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. db file in the directory where your code lives. Here's a quick example of how SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. Setup Jul 12, 2024 · Let's work together to solve this problem! To resolve the issues with creating an SQL agent using LangChain, you can follow these steps: Correct the create_sql_agent Function Call: Ensure that the parameters passed to the create_sql_agent function are correct. Must provide exactly one of ‘toolkit’ or Large databases In order to write valid queries against a database, we need to feed the model the table names, table schemas, and feature values for it to query over. Toolkit is created using ‘db’ and 🌐 SQL Database - Databricks SQL is integrated with SQLDatabase in LangChain, allowing you to access the auto-optimizing, exceptionally performant data warehouse. The first framework i used for this was Langchain. messages import AIMessage, SystemMessage from langchain_core. , data incorporating relations among entities and variables. With the combination of LangChain, SQL Agents, and SQL Using SQL to interact with CSV data is the recommended approach because it is easier to limit permissions and sanitize queries than with arbitrary Python. This method uses toolkit instead of simple list of tools. May 13, 2024 · The agent successfully utilized the Dataherald text-to-SQL tool to generate the SQL query and then proceeded to generate a plot based on the results obtained from executing the SQL query. Aug 30, 2024 · Using LangChain and OpenAI in conjunction with an SQL database can simplify the process of querying and analyzing data. Setup: Install langchain-community. Contribute to langchain-ai/langgraph development by creating an account on GitHub. prompts. This is often achieved via tool-calling. db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. 27 agent_toolkits create_sql_agent Dec 13, 2024 · 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 from sql_agent. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. 3. Jun 24, 2024 · LangChain offers an SQL Agent that allows for more flexible interactions with SQL databases. Mar 10, 2025 · 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. agent. Sep 12, 2023 · 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. Feb 19, 2024 · LangChain is an open-source framework for creating applications that use and are powered by language models (LLM/MLM/SML). prompts import BasePromptTemplate, PromptTemplate from langchain_core. Jun 17, 2025 · Build an Agent LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. Instead, we must find ways to dynamically insert into the prompt Dec 1, 2024 · QuerySQLDataBaseTool: A LangChain utility for executing SQL commands on the connected database. Most SQL databases make it easy to load a CSV file in as a table (DuckDB, SQLite, etc. Tools within the SQLDatabaseToolkit are designed to interact with a SQL database. Usage: Run after validating queries to retrieve specific data or perform updates. You are an agent designed to interact with a SQL database. Build resilient language agents as graphs. toolkit. The post has a really helpful walkthrough (with code!) to bring the ideas to life. Essentially, langchain makes it easier to build chatbots for your own data and "personal assistant" bots that Jul 13, 2023 · LangChain SQL Agent Introduction Natural language querying provides users with a more intuitive and efficient way to interact with databases. Finally, this retrieved context is passed onto the LLM along with the prompt and voila! we have a working SQL Agent that can look into your chat history. How to do Text-to-SQL in LangChain? Aug 19, 2023 · Natural language querying allows users to interact with databases more intuitively and efficiently. Aug 21, 2023 · 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. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs In this guide we'll go over prompting strategies to improve SQL query generation using createsqlquerychain. Trajectory: Evaluate whether the agent took the expected path (e. """ from __future__ import annotations from typing import ( TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union, cast, ) from langchain_core. Jun 21, 2023 · In our last blog post we discussed the topic of connecting a PostGres database to Large Language Model (LLM) and provided an example of how to use LangChain SQLChain to connect and ask questions This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. To set it up, follow these instructions, placing the . Very easy to implement: Its pretty convenient to import LLMs Gpt, Claude, Gemini. The AgentExecutor then executes the task, invoking the necessary functions to interact with the database and handle the output. The main difference between the two is that our agent can query the database in a loop as many time as it needs to answer the question. This setup allows you to interact with complex databases using natural language, making data analysis more accessible to everyone, regardless of their SQL expertise. Single step: Evaluate any agent step How to do query validation as part of SQL question-answering Perhaps the most error-prone part of any SQL chain or agent is writing valid and safe SQL queries. Once you've done this you can use all of the chain and agent-creating techniques outlined in the SQL use case guide. Learn how to build a question/answering system over SQL data using LangChain, a framework for building AI applications. 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). In this first example we will use slightly different type of agent - SQL Agent which can be instantiated with it's own method create_sql_agent. sql. Apr 24, 2023 · Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. They enable use cases such as: Generating queries that will be run based on natural language questions, Creating May 15, 2024 · Here, we offer a step-by-step guide on how to use LangChain to implement text-to-SQL, and how to handle any challenges that come your way. May 7, 2024 · Here we use our SQL Agent that will directly run queries on your MySQL database and get the required data. SQL One of the most common types of databases that we can build Q&A systems for are SQL databases. Here are some relevant links: 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. Mar 10, 2025 · 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 . In this post, basic LangChain components (toolkits, chains, agents) will be used to create a natural language to SQL prompt that will allow interactions with an Azure SQL Database; just ask the database what you want as if speaking to another person. If agent_type is “tool-calling” then llm is expected to support tool calling. A common application is to enable agents to answer questions using data in a relational database, potentially in an agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. TheAILearner demonstrates the entire process from setup to querying databases. Parameters llm (BaseLanguageModel) – Language model to use for the agent. e. This was my first time writing an agent with a good and serious usecase. To set up this agent, we use the create_sql_agent function, which includes the SQLDatabaseToolkit. Getting Started Jul 11, 2023 · Everything about SQL Agent LangChain and how to do database querying using natural language for easier interaction. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. Additionally, it integrates with This example shows how to load and use an agent with a SQL toolkit. We will cover: Appending a "query validator" step to the query generation; Prompt engineering to reduce the incidence of errors. This method allows you to save the context of a conversation, which can be used to respond to queries, retain history, and remember context for subsequent queries. The function create_sql_agent you've used in your code is designed to construct a SQL agent from a language model and a toolkit or database. SQLDatabaseToolkit # class langchain_community. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. Sep 28, 2023 · Usually it is an iterative process until the Agent reaches the Final Answer or output. SQLDatabaseToolkit [source] # Bases: BaseToolkit SQLDatabaseToolkit for interacting with SQL databases. extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. It can recover from errors by running a generated query, catching the traceback and regenerating it SQLDatabase Toolkit This will help you get started with the SQL Database toolkit. LangChain Python API Reference langchain-community: 0. Feb 19, 2024 · I hope all's been well on your side! Yes, it is indeed possible to create an SQL agent in the latest version of LangChain to query tables on Google BigQuery. This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions. Today, let’s dive deeper into the inner workings of this agent. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. ). Execute SQL query: Execute the SQL query. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and Sep 10, 2024 · Learn how to build a SQL Agent using Llama 3, Langchain, and Ollama. Specifically, check the equality operator (==) used for llm and correct it to a single =. In this tutorial we Mar 24, 2024 · Based on the code you've provided and the context given, the slow response times you're experiencing when using the create_sql_agent function could be due to the process of embedding the query into a vector representation. Sep 28, 2023 · In this article, I will show you how we can use LangChain Agent and Azure OpenAI gpt-35-turbo model to query your SQL database using natural language (without writing any SQL at all!) and get Dec 9, 2024 · """SQL agent. Mar 7, 2025 · Building a Data Visualization Agent with LangChain, ChatGroq/Gemini, SQLAgent, FastAPI, and React An AI-driven approach -- Positions it as a practical guide Subhojyoti Singha 12 min read Sep 4, 2024 · We create the SQL agent using the previously defined tools and prompt. May 16, 2024 · At a high-level, the steps of any SQL chain and agent are: Convert question to SQL query: Model converts user input to a SQL query. Follow the steps to create a chain or an agent that converts questions to SQL queries and returns natural language answers. We'll largely focus on methods for getting relevant database-specific information in your prompt. Sep 21, 2023 · To add memory to the SQL agent in LangChain, you can use the save_context method of the ConversationBufferMemory class. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. ⚠️ Security note ⚠️ Building Q&A You are an agent designed to interact with a SQL database. SQL In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. For detailed documentation of all SQLDatabaseToolkit features and configurations head to the API reference. LangChain / LangGraph SQL Agent Demo This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. In this guide we'll go over some strategies for validating our queries and handling invalid queries. When there are many tables, columns, and/or high-cardinality columns, it becomes impossible for us to dump the full information about our database in every prompt. It is particularly useful in handling structured data, i. Mar 13, 2023 · 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. The wrapper provides a simple interface to execute SQL queries and fetch results. This notebook shows how to use agents to interact with Spark SQL. You can read more about them in the documentation. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. This app will generate SQL queries using an LLM, execute Dec 9, 2024 · Examples using SQLDatabase ¶ Build a Question/Answering system over SQL data CnosDB How to better prompt when doing SQL question-answering How to deal with large databases when doing SQL question-answering How to do query validation as part of SQL question-answering How to do question answering over CSVs Rebuff SQL Database In this guide we'll go over prompting strategies to improve SQL query generation. Other agents will be instantiated in more generic way as we will see below in other examples. Note that, as this agent is in active development, all answers might not be correct. create_sql_agent (llm [, ]) Construct a SQL agent from an LLM and toolkit or database. eqbannqslbkccdofwanetsqzflvvfolghjxmeaiwrmjmlbiih