Langchain sql agent examples. Must provide exactly one of ‘toolkit’ or .
- Langchain sql agent examples. 0. For example, the list_tables_tool points to the same code as if we import from langchain_community. Nov 20, 2024 路 We will explore how to use LangGraph within Langchain framework for multi agent setup and use openAI models for SQL query construction and retrieving information. Must provide exactly one of ‘toolkit’ or In this guide we'll go over prompting strategies to improve SQL query generation. messages import AIMessage, SystemMessage from langchain_core. The power of this approach lies in its flexibility and 馃摝 Step 1: Install Dependencies bash Copy Edit pip install openai langchain sqlalchemy streamlit 馃 Step 2: Python Code (In-Memory Trade Assistant) Save this as nl_sql_assistant. Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. prompts. ts - Agent-based SQL querying with formatted output examples_of_langchain_db_llm - Advanced graph-based query processing examples SQLDatabaseToolkit # class langchain_community. You can read more about them in the documentation. - tryAGI/LangChain An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. We will also demonstrate how to use few-shot prompting in this context to improve performance. These systems will allow us to ask a question about the data in a database and get back a natural language answer. agents. db). extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. Samples on how to use the langchain_sqlserver library with SQL Server or Azure SQL as a vector store are: test-1. Sep 28, 2023 路 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 Example application for the construction and inference of an LLM-based LangChain SQL Agent that can dynamically query a database and invoke multiple visualization tools. This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. sql Test SELECT * FROM Artist LIMIT 10; Now, Chinhook. Azure OpenAI GPT-4 for intelligent language understanding and generation of SQL queries in PostgreSQL. toolkit. Learn to build AI agents with LangChain and LangGraph. How to do Text-to-SQL in LangChain? Mar 10, 2025 路 Building Q&A Agent with Text-to-SQL Using LangChain We will use LangChain’s Runnable API and StructuredOutputParser to generate the necessary SQL queries to answer user questions. May 7, 2024 路 Today, I’ll introduce you to another amazing dimension of RAG where instead of documents, we will be retrieving data directly from a MySQL database. The main advantages of using SQL Agents are: It can answer questions based on the databases schema as well as on the databases content (like describing a specific table). These systems will allow us to ask a question about the data in a graph database and get back a natural language answer. First, let us see the current SOTA text to sql workflow: Schema and Metadata Extraction: The system processes the provided database (e. If you want to get automated tracing from runs of individual tools May 16, 2024 路 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. In this tutorial we How to better prompt when doing SQL question-answering In this guide we'll go over prompting strategies to improve SQL query generation using create_sql_query_chain. We hope to continue developing different toolkits that can enable agents to do amazing feats. 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. Productionization Mar 31, 2024 路 The above video shows how SQL LLM agent is interacting with sqlite DB This blog introduces an agent that communicates with SQL databases, eliminating the need to know the schema beforehand. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. Aug 19, 2023 路 Introduction Natural language querying allows users to interact with databases more intuitively and efficiently. . In this guide we'll go over the basic ways to create a Q&A system over tabular data in databases. I am able to use create_sql_query_chain just fine against either an OpenAI LLM or Sep 11, 2023 路 Depending on the user input, the agent can then decide which, if any, of these tools to call. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. To use Google BigQuery, you would need to create an instance of SQLDatabase that connects to your Google BigQuery database and pass it to the create_sql_agent function. These are applications that can answer questions about specific source information. However, with accurate prompts, Gemini models can generate answers based on the data. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. In this example you find where sql_code is defined or created in the tool run, then send it to the run manager. tool. It can understand natural language questions, convert them into SQL queries, execute the queries, and present the results in a user-friendly format. Aug 30, 2024 路 Using LangChain and OpenAI in conjunction with an SQL database can simplify the process of querying and analyzing data. Essentially, langchain makes it easier to build chatbots for your own data and "personal assistant" bots that This n8n workflow demonstrates how to create an agent using LangChain and SQLite. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. MPT, from MosaicML) to query Databricks SQL. , SQLite or CSV Jul 12, 2024 路 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. With features like tool use, memory, and chaining, LangChain makes it easy to prototype and scale intelligent agents. To set it up follow the instructions on https://database. The below example will use a SQLite connection with Chinook database. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. """ from __future__ import annotations from typing import ( TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union, cast, ) from langchain_core. Jan 31, 2025 路 By the end of this guide, you will have a fully functional Text-to-SQL web app that allows users to upload datasets, type queries in natural language, and generate optimized SQL queries automatically. Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. Sep 28, 2023 路 Usually it is an iterative process until the Agent reaches the Final Answer or output. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook. In this guide we'll go over prompting strategies to improve SQL query generation. MLflow's LangChain Integration streamlines the process of developing and operating modern compound ML systems. Dec 9, 2024 路 Construct a SQL agent from an LLM and toolkit or database. The… Mar 11, 2024 路 Unlock the full potential of database interactions with our guide on Natural Language to SQL using LangChain and LLM. GitHub Gist: instantly share code, notes, and snippets. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). sql_database. To set up this agent, we use the create_sql_agent function, which includes the SQLDatabaseToolkit. langchain. How to: pass in callbacks at runtime How to: attach callbacks to a module How to: pass callbacks into a module constructor How to: create custom callback handlers How to: use callbacks in The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. ) agent_executor Introduction LangChain is a framework for developing applications powered by large language models (LLMs). 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). Follow these installation steps to create Chinook. format_scratchpad import format_log_to_str from langchain. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. sql Run sqlite3 Chinook. The application showcases a shipping company Tutorials New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. Dec 9, 2024 路 This agent can understand natural language questions, generate and validate SQL queries, and provide answers based on database content. question-answering) agent, we'll use a simple ReACT architecture and give the agent tools for looking up track names, artist names, and album names based on various filters. Dec 9, 2024 路 Key init args: db: SQLDatabase The SQL database. Contribute to langchain-ai/langsmith-cookbook development by creating an account on GitHub. Contribute to langchain-ai/langgraph development by creating an account on GitHub. Jul 12, 2024 路 SQL agent generates the SQL query and stops, not calling sql_db_query to execute it. Additionally, it integrates with Langsmith for tracing and feedback collection. llms import OpenAI, SQLDatabase db = SQLDatabase() db_chain = SQLDatabaseChain. These applications use a technique known as Retrieval Augmented Generation, or RAG. For example, you can look up albums by a particular artist, artists who released songs with a specific name, etc. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language This template enables a user to interact with a SQL database using natural language. 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 SQLDatabase Toolkit May 21, 2025 路 Learn to build AI agents using Langchain MCP’s model context protocol, including code examples, and best practices|ProjectPro LangGraph docs on common agent architectures Pre-built agents in LangGraph Legacy agent concept: AgentExecutor LangChain previously introduced the AgentExecutor as a runtime for agents. 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. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. Example Input: table1, table2, table3', db=<langchain_community. Feb 19, 2024 路 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. Each project is presented in a Jupyter notebook and showcases various functionalities such as creating simple chains, using tools, querying CSV files, and interacting with SQL databases. 馃挭 馃殌 Setup Run the top part of the workflow once. Oct 24, 2024 路 The prefix parameter is primarily used to format the initial part of the prompt, providing instructions for the agent on how to interact with a SQL database [2] [3] [4]. Parameters llm (BaseLanguageModel) – Language model to use for the agent. From tools to agent loops—this guide covers it all with real code, best practices, and advanced tips. from_uri(. This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions. SQLDatabaseToolkit # class langchain_community. If you're looking to get started with chat models, vector stores, or other LangChain components from a specific provider, check out our supported One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. The SQLDatabaseChain can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, and SQLite. Jul 12, 2025 路 Files file. agent_toolkits. create_sql_agent( llm: BaseLanguageModel, toolkit: SQLDatabaseToolkit | None = None, agent_type: AgentType | Literal['openai-tools', 'tool-calling'] | None = None, callback_manager: BaseCallbackManager | None = None, prefix: str | None = None, suffix: str | None = None, format_instructions: str | None = None, input_variables: List Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. Getting Started This example shows how to load and use an agent with a SQL toolkit. Build resilient language agents as graphs. It aids interaction with vector databases, APIs, PDFs, SQL databases, and many more. Tools are essentially functions that extend the agent’s capabilities by Apr 9, 2024 路 The docs describe how to create an SQL agent using OpenAI as an example but implying that the approach is generic. 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. SQLDatabase object at 0x103d5fa60>), ListSQLDatabaseTool(db=<langchain_community. db file in a notebooks folder at the root of this repository. We also released the langchain-sqlserver package, enabling the management of SQL Server as a Vectorstore in LangChain. Refer to AI Agent for more information on the AI Agent node itself. This is often achieved via tool-calling. Mar 7, 2025 路 Final Thoughts By integrating LangChain, Vector DB, and Federated Queries, this AI-driven Text-to-SQL system provides: Understands natural language queries 10x faster query execution through pre Aug 26, 2023 路 I need to connect my langchain database agent with my sqlserver database so that my agent can access the data, yet in the documention it does not explain how to do so, it only shows how to connect Sep 5, 2023 路 For example, the standard SQL Toolkit draws from standard best practices that have been extensively covered in this blogpost. The language model used is OpenAIs GPT-4o mini. This agent is valuable for building natural language interfaces to databases. This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. create_sql_agent (llm [, ]) Construct a SQL agent from an LLM and toolkit or database. You could also just append the sql code as a string/json to the output itself to return it in the typical agent view. Jun 15, 2023 路 Since LangChain uses SQLAlchemy to connect to SQL databases, we can use any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, Databricks, or SQLite. We'll largely focus on methods for getting relevant database-specific information in your prompt. read Chinook_Sqlite. SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. db Run . While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. Jun 28, 2024 路 There might be confusion here. Setup First, install the required packages and set your environment variables. Aug 21, 2023 路 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. Example from langchain_experimental. > Entering new AgentExecutor chain Action: list_tables_sql_db Action Input: "" . May 4, 2025 路 Building agentic AI systems using LangChain allows developers to create powerful, autonomous workflows that go beyond simple text generation. This method uses toolkit instead of simple list of tools. Toolkit is created using ‘db’ and Agents 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. In this guide we'll go over the basic ways to create a Q&A chain over a graph database. Get started Familiarize yourself with LangChain's open-source components by building simple applications. 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. They enable use cases such as: Generating queries that will be run based on natural language questions, Creating Apr 24, 2023 路 Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. Nov 14, 2023 路 LangChain SQL - Agent Setup. example as a template. Let’s whip out LangChain. but that does't work in MS SQL database. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. tools. Be Explore a practical example of using Langchain's SQL agent to enhance data interactions and streamline queries. Aug 21, 2023 路 Query Generation: LangChain translates user intent into structured SQL queries that precisely fetch relevant data. prompts import PromptTemplate template = '''Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. This setup allows you to interact with complex databases using natural language, making data analysis more accessible to everyone, regardless of their SQL expertise. sql_agent. 157 is it possible to use LangChain’s SQL Database Agent with Databricks SQL and Dolly? If so, could someone kindly advise what the Python syntax would be? db = SQLDatabase. test-2. Dec 1, 2024 路 How to build an AI-Powered SQL Data Analysis Agent with LangChain and CrewAI Mar 13, 2023 路 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. agents import AgentExecutor from langchain. env. First, we will show a simple out-of-the-box option and then implement a more sophisticated version with LangGraph. SQLDatabaseToolkit [source] # Bases: BaseToolkit SQLDatabaseToolkit for interacting with SQL databases. Aug 16, 2023 路 This blog delves into the intriguing synergy between LangChain, an innovative language interface, and a robust language model, to effortlessly query the Oracle Database. May 5, 2023 路 Hi there, referring to this LangChain document here: SQL Database Agent — 馃馃敆 LangChain 0. LangChain comes with a built-in chain for this: create_sql_query_chain. Setup: Install langchain-community. Execute SQL query: Execute the SQL query. It can recover from errors by running a generated query Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) Text Splitter Playground - Play with various types of text splitting for RAG (Source code) Tweet May 13, 2024 路 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. For this, four datasets from the European Statistical Office (Eurostat) are loaded Build an Extraction Chain In this tutorial, we will use tool-calling features of chat models to extract structured information from unstructured text. Oct 1, 2024 路 In this tutorial, you will learn how to create a message history and a UI for a LangChain chatbot application. agents Apr 26, 2025 路 LangGraph is a part of the LangChain eco-system which focuses on creating directed graphs rather than a chain to build agents. Specifically, check the equality operator (==) used for llm and correct it to a single =. """LangChain SQL Agent In this example, we first create an SQL database with a ‘countries’ table, and subsequently, we will use LangChain Agent to make queries against it. Query Execution: Query is executed in Snowflake to obtain the insights. from_llm(OpenAI(), db) Security note: Make sure that the database connection uses credentials that are narrowly-scoped to only include the permissions this chain needs. g. Sep 5, 2024 路 However, this was just an illustration. Note that, as this agent is in active development, all answers might not be correct. Toolkit is created using ‘db’ and Jun 17, 2025 路 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. Must provide exactly one of ‘toolkit’ or create_sql_agent # langchain_community. I followed this example: https://python. py: Basic sample to store vectors, content and metadata into SQL Server or Azure SQL and then do simple similarity searches. from langchain_core. Nov 19, 2024 路 LangChain Integration for Vector Support for Azure SQL and SQL database in Microsoft Fabric Microsoft SQL now supports native vector search capabilities in Azure SQL and SQL database in Microsoft Fabric. Mar 7, 2025 路 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 Follow. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. Jul 30, 2024 路 We will create an agent using LangChain’s capabilities, integrating the LLAMA 3 model from Ollama and utilizing the Tavily search tool for web search functionalities. llm: BaseLanguageModel The language model (for use with QuerySQLCheckerTool) Instantiate: SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. We try to be as close to the original as possible in terms of abstractions, but are open to new entities. About Langchain SQL agent example to talk to postgresql database in natural language C# implementation of LangChain. Mar 10, 2025 路 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 . We will use a handy SQL database wrapper available in the langchain_community package to interact with the database. db file in the directory where your code lives. Toolkits are supported Sep 12, 2024 路 The entire workflow is orchestrated using LangGraph Cloud, which provides a framework for easily building complex AI agents, a streaming API for real-time updates, and a visual studio for monitoring and experimenting with the agent's behavior. This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. Jun 24, 2024 路 LangChain offers an SQL Agent that allows for more flexible interactions with SQL databases. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. We will cover: How the dialect of the LangChain SQLDatabase impacts the prompt of the chain; How to format schema information into 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. TLDR; this repo contains some starter examples for working with Langchain and LLM Instruction models (e. py: python Copy Edit import os from sqlalchemy import create_engine, MetaData, Table, Column, Integer, String, Date Jan 29, 2024 路 However, I wasn't able to find any information on whether the create_sql_agent function in LangChain supports the addition of a FewShotPromptTemplate directly. We will cover implementations using both chains and agents. SQL One of the most common types of databases that we can build Q&A systems for are SQL databases. guide/2-sample-databases-sqlite/, placing the . sql. LangChain’s ecosystem While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. """ import contextlib from tempfile import TemporaryFile from dotenv import load_dotenv from langchain. This example will use OpenAI as the LLM. Create autonomous workflows using memory, tools, and LLM orchestration. Oct 29, 2024 路 To customize the prompt template for the SQL query agent and achieve better results, you can follow these steps: Modify the Prompt Template: Ensure your prompt template is correctly structured and includes all necessary variables. Setup This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. env using . agent. 馃寪 SQL Database - Databricks SQL is integrated with SQLDatabase in LangChain, allowing you to access the auto-optimizing, exceptionally performant data warehouse. It is then wrapper into ToolNode to make it a node with fallback. Lambda instruments the Financial Services agent logic as a LangChain Conversational Agent that can access customer-specific data stored on DynamoDB, curate opinionated responses using your documents and webpages indexed by Kendra, and provide general knowledge answers through the FM on Bedrock. Let’s select a chat model for our application: Dec 9, 2024 路 Construct a SQL agent from an LLM and toolkit or database. base. It's possible that this feature is not yet implemented or that I may have missed it. Other agents will be instantiated in more generic way as we will see below in other examples. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Aug 10, 2023 路 So I was trying to write a code using Langchain to query my Postgres database and it worked perfectly then I tried to visualize the data if the user prompts like "Plot bar chart" now for Aug 25, 2024 路 In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. Ready to support ollama. However, there is still room for improvement when it comes to building a custom solution and adjusting the generic tools to the specific use case. Dec 5, 2023 路 In my previous blog, we explored the Langchain tool and its remarkable create_sql_agent function, which enables the creation of a powerful… Apr 2, 2025 路 You can expose SQL or Python functions in Unity Catalog as tools for your LangChain agent. Jan 3, 2025 路 Synergy Between LangChain and AWS Bedrock By combining LangChain agents with AWS Bedrock models, developers can unlock unparalleled potential for generative AI applications. LangChain LangChain is a tool that helps building chatbots, RAG methods, and other LLM-based tools. It is designed to answer more general questions about a database, as well as recover from errors. (Update when i a Nov 21, 2023 路 I want to use sql Agent together with few shot examples. Be careful running it on sensitive data! This uses the example Chinook database. utilities. For full guidance on creating Unity Catalog functions and using them in LangChain, see the Databricks UC Toolkit documentation. LangChain Framework: Powers the agent architecture, allowing seamless integration of RAG and Lookup agent For the lookup (i. Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. Here are some relevant links: May 15, 2024 路 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. Dec 9, 2024 路 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 SQL Database This notebook showcases an agent designed to interact with a SQL databases. MS SQL should be something like select top 10 * from cache_instances. chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder SQL Chain example # This example demonstrates the use of the SQLDatabaseChain for answering questions over a database. Under the hood, the Langchain SQL Agent uses a MRKL (pronounced Miracle)-based approach, and queries the database schema and example rows and uses these to generate SQL queries, which it then executes to pull back the results you're asking for. Adding into this, we are also solving another This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. ts - Basic SQL query generation using generate_sql_query function agent. Through the use of the Gemini API, you will be able retrieve necessary information by chatting with a SQL database. Then still return the sql output like normal. How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph Callbacks Callbacks allow you to hook into the various stages of your LLM application's execution. Feb 19, 2024 路 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. Reading an SQL database can be challenging for humans. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. db is in our directory and we can interface with it using the Typeorm-driven SqlDatabase class: This repository demonstrates how to build a multi-agent AI system using: LangChain for natural language to SQL translation. py: Read books reviews from a file, store it in SQL Server or Azure SQL, and then do Convert question to SQL query The first step is to take the user input and convert it to a SQL query. Make sure the create an . AutoGen for coordinating AI agents in collaborative workflows. db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. SQLDatabase object at 0x103d5fa60>), QuerySQLCheckerTool(description='Use this tool to double check if your query is correct before executing it. To set it up, follow these instructions, placing the . Let’s move onto something a little bit more advanced and complex. If you want to guide the SQL agent with specific examples, you might consider incorporating these examples into the prompt in a way that the language model can use them as context. Jun 19, 2025 路 Build AI agents from scratch with LangChain and OpenAI. If agent_type is “tool-calling” then llm is expected to support tool calling. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. db in the same directory as this notebook: Save this file as Chinook_Sqlite. In this step-by-step tutorial, we will show […] Sep 18, 2024 路 Let’s walk through a simple example of building a Langchain Agent that performs two tasks: retrieves information from Wikipedia and executes a Python function. LangChain provides a agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. Toolkit is created using ‘db’ and A powerful text-to-SQL agent that converts natural language queries into SQL statements using LangGraph and LangChain Apr 2, 2025 路 You can expose SQL or Python functions in Unity Catalog as tools for your LangChain agent. Azure Database for PostgreSQL for data storage and querying. The SQL Agent provided by LangChain is a tool that allows you to interact with SQL databases using natural language. It can recover from errors by running a generated query, catching the traceback and regenerating it Jun 21, 2023 路 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 Dec 13, 2024 路 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 agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. The SQL Agent uses a SQL database as a data source. However, it is not perfect. The wrapper provides a simple interface to execute SQL queries and fetch results. com/docs/use_cases/qa_structured/sql#extending-the-sql Apr 7, 2024 路 Deploying agents with Langchain is a straightforward process, though it is primarily optimized for integration with OpenAI’s API. e. Sep 7, 2024 路 Technologies and Tools Azure Cognitive Services: Facilitates document retrieval for financial analysis. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Sep 12, 2023 路 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. Mar 1, 2023 路 Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). sql import SQLDatabaseChain from langchain_community. Dec 9, 2024 路 """SQL agent. Jan 20, 2025 路 seems like by default, the LLM generate SQL with mysql syntax - for example SELECT * FROM cache_instances LIMIT 10. 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. To reliably obtain SQL queries (absent markdown formatting and explanations or clarifications), we will make use of LangChain’s structured output abstraction. aqcaqw mrbynu jhki ijiokc dsts bni eype iisizfv qdkvhx vfybt