Langchain sql agent examples. Toolkit is created using ‘db’ and .

Langchain sql agent examples. You can read more about them in the documentation. Contribute to langchain-ai/langsmith-cookbook development by creating an account on GitHub. agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. However, with accurate prompts, Gemini models can generate answers based on the data. Toolkit is created using ‘db’ and Tutorials New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. This n8n workflow demonstrates how to create an agent using LangChain and SQLite. SQLDatabaseToolkit # class langchain_community. This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. 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 Explore a practical example of using Langchain's SQL agent to enhance data interactions and streamline queries. 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 Sep 18, 2024 · 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. tool. We'll largely focus on methods for getting relevant database-specific information in your prompt. May 7, 2024 · Here we use our SQL Agent that will directly run queries on your MySQL database and get the required data. We hope to continue developing different toolkits that can enable agents to do amazing feats. Sep 12, 2024 · 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. These are applications that can answer questions about specific source information. 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 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. Mar 10, 2025 · 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. To set up this agent, we use the create_sql_agent function, which includes the SQLDatabaseToolkit. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language MLflow's LangChain Integration streamlines the process of developing and operating modern compound ML systems. Aug 21, 2023 · A step-by-step guide to building a LangChain enabled SQL database question answering agent. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). 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_db_query_checker: Use this tool to double check if your query is correct before executing it. utilities. sql_database. Get started Familiarize yourself with LangChain's open-source components by building simple applications. The language model used is OpenAIs GPT-4o mini. sql Test SELECT * FROM Artist LIMIT 10; Now, Chinhook. SQLDatabase object at 0x103d5fa60>), QuerySQLCheckerTool(description='Use this tool to double check if your query is correct before executing it. This method uses toolkit instead of simple list of tools. Through the use of the Gemini API, you will be able retrieve necessary information by chatting with a SQL database. This is often achieved via tool-calling. sql. SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. Learn how to build 3 types of planning agents in LangGraph in this post. Toolkits are supported 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. The application showcases a shipping company 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. base. Dec 1, 2024 · How to build an AI-Powered SQL Data Analysis Agent with LangChain and CrewAI """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. To reliably obtain SQL queries (absent markdown formatting and explanations or clarifications), we will make use of LangChain’s structured output abstraction. env using . If agent_type is “tool-calling” then llm is expected to support tool calling. Example Input: table1, table2, table3', db=<langchain_community. Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples Jun 24, 2024 · LangChain offers an SQL Agent that allows for more flexible interactions with SQL databases. It can recover from errors by running a generated query In this guide we'll go over prompting strategies to improve SQL query generation using createsqlquerychain. Query Execution: Query is executed in Snowflake to obtain the insights. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. This repository demonstrates how to build a multi-agent AI system using: LangChain for natural language to SQL translation. ts - Agent-based SQL querying with formatted output examples_of_langchain_db_llm - Advanced graph-based query processing examples Oct 1, 2024 · In this tutorial, you will learn how to create a message history and a UI for a LangChain chatbot application. 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. Setup This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. SQLDatabaseToolkit [source] # Bases: BaseToolkit SQLDatabaseToolkit for interacting with SQL databases. agent_toolkits. db Run . create_sql_agent # langchain_community. Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. agents. In this step-by-step tutorial, we will show […] C# implementation of LangChain. Mar 11, 2024 · Unlock the full potential of database interactions with our guide on Natural Language to SQL using LangChain and LLM. 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. GitHub Gist: instantly share code, notes, and snippets. 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. """ import contextlib from tempfile import TemporaryFile from dotenv import load_dotenv from langchain. TLDR; this repo contains some starter examples for working with Langchain and LLM Instruction models (e. While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook. Build an Extraction Chain In this tutorial, we will use tool-calling features of chat models to extract structured information from unstructured text. You could also just append the sql code as a string/json to the output itself to return it in the typical agent view. It can recover from errors by running a generated query, catching the traceback and regenerating it 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 Make sure the create an . It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. For full guidance on creating Unity Catalog functions and using them in LangChain, see the Databricks UC Toolkit documentation. Sep 12, 2023 · 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. LangChain provides a Nov 20, 2024 · 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. I followed this example: https://python. 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 One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. 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 Reading an SQL database can be challenging for humans. Azure Database for PostgreSQL for data storage and querying. We try to be as close to the original as possible in terms of abstractions, but are open to new entities. prompts import ChatPromptTemplate system_message = """ Given an input question, create a syntactically correct {dialect} query to run to help find the answer. This notebook showcases an agent designed to interact with a SQL databases. sql Run sqlite3 Chinook. Always use this tool before executing a query with sql_db_query! SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. Must provide exactly one of ‘toolkit’ or This example shows how to load and use an agent with a SQL toolkit. 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. Ready to support ollama. extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. , SQLite or CSV Jun 28, 2024 · There might be confusion here. Let’s select a chat model for our application: 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 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. 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). We also released the langchain-sqlserver package, enabling the management of SQL Server as a Vectorstore in LangChain. read Chinook_Sqlite. Build resilient language agents as graphs. Mar 10, 2025 · LangChain is an excellent framework equipped with components and third-party integrations for developing applications that leverage LLMs. LangChain comes with a built-in chain for this: create_sql_query_chain. Here are some relevant links: You are an agent designed to interact with a SQL database. agents Apr 2, 2025 · You can expose SQL or Python functions in Unity Catalog as tools for your LangChain agent. If you want to get automated tracing from runs of individual tools Mar 1, 2023 · 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 Database - Databricks SQL is integrated with SQLDatabase in LangChain, allowing you to access the auto-optimizing, exceptionally performant data warehouse. For this, four datasets from the European Statistical Office (Eurostat) are loaded Apr 24, 2023 · Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. 🗣️ Chatting with Your Data Send a Sep 5, 2024 · In this post I want to explore how one might go about prompt engineering to retrieve more accurate results incorporating a local SQL database, return a SQL how to use LangChain to talk to a local database and return not just a SQL query, but also get the model to run the SQL query and return the answer in text form. py: Basic sample to store vectors, content and metadata into SQL Server or Azure SQL and then do simple similarity searches. langchain. In this guide we'll go over prompting strategies to improve SQL query generation. 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). db is in our directory and we can interface with it using the Typeorm-driven SqlDatabase class: Example Input: table1, table2, table3 sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database. If you're looking to get started with chat models, vector stores, or other LangChain components from a specific provider, check out our supported Feb 19, 2024 · LangChain is an open-source framework for creating applications that use and are powered by language models (LLM/MLM/SML). Samples on how to use the langchain_sqlserver library with SQL Server or Azure SQL as a vector store are: test-1. Aug 10, 2023 · 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 &quot;Plot bar chart&quot; now for Jan 3, 2025 · Synergy Between LangChain and AWS Bedrock By combining LangChain agents with AWS Bedrock models, developers can unlock unparalleled potential for generative AI applications. Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. The function accepts parameters like llm, toolkit, agent_type, callback_manager, prefix, suffix, format_instructions, input_variables, top_k, max_iterations, max_execution_time, early_stopping In this example you find where sql_code is defined or created in the tool run, then send it to the run manager. Parameters llm (BaseLanguageModel) – Language model to use for the agent. db file in the directory where your code lives. Sep 28, 2023 · Usually it is an iterative process until the Agent reaches the Final Answer or output. db in the same directory as this notebook: Save this file as Chinook_Sqlite. 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. In this tutorial we 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 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. (Update when i a Nov 21, 2023 · I want to use sql Agent together with few shot examples. Dec 9, 2024 · Construct a SQL agent from an LLM and toolkit or database. Sep 5, 2023 · For example, the standard SQL Toolkit draws from standard best practices that have been extensively covered in this blogpost. agents import AgentExecutor from langchain. This template enables a user to interact with a SQL database using natural language. Note that, as this agent is in active development, all answers might not be correct. Aug 19, 2023 · Introduction Natural language querying allows users to interact with databases more intuitively and efficiently. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. Setup: Install langchain-community. com/docs/use_cases/qa_structured/sql#extending-the-sql 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… This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. For example, the list_tables_tool points to the same code as if we import from langchain_community. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. Then still return the sql output like normal. First, let us see the current SOTA text to sql workflow: Schema and Metadata Extraction: The system processes the provided database (e. Execute SQL query: Execute the SQL query. 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. Aug 30, 2024 · Using LangChain and OpenAI in conjunction with an SQL database can simplify the process of querying and analyzing data. Specifically, check the equality operator (==) used for llm and correct it to a single =. format_scratchpad import format_log_to_str from langchain. Toolkit is created using ‘db’ and 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 This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. 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. In this article, we will build an AI workflow using LangChain and construct an AI agent workflow by issuing SQL queries on CSV data with DuckDB. The SQL Agent provided by LangChain is a tool that allows you to interact with SQL databases using natural language. They enable use cases such as: Generating queries that will be run based on natural language questions, Creating Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. Other agents will be instantiated in more generic way as we will see below in other examples. 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. AutoGen for coordinating AI agents in collaborative workflows. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. MPT, from MosaicML) to query Databricks SQL. ts - Basic SQL query generation using generate_sql_query function agent. Files file. The below example will use a SQLite connection with Chinook database. We will also demonstrate how to use few-shot prompting in this context to improve performance. db). How to do Text-to-SQL in LangChain? 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. example as a template. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. These applications use a technique known as Retrieval Augmented Generation, or RAG. from langchain_core. This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. Azure OpenAI GPT-4 for intelligent language understanding and generation of SQL queries in PostgreSQL. SQLDatabase object at 0x103d5fa60>), ListSQLDatabaseTool(db=<langchain_community. 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. Getting Started The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. test-2. To set it up, follow these instructions, placing the . Feb 19, 2024 · 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. Contribute to langchain-ai/langgraph development by creating an account on GitHub. Jan 31, 2024 · Based on the provided context, it appears that the create_sql_agent function in LangChain does not have a parameter to accept few shot examples directly. db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. Apr 2, 2025 · You can expose SQL or Python functions in Unity Catalog as tools for your LangChain agent. 💪 🚀 Setup Run the top part of the workflow once. Tools are essentially functions that extend the agent’s capabilities by Nov 19, 2024 · 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. Toolkit is created using ‘db’ and Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. . 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 9, 2024 · Return type Runnable [Union [SQLInput, SQLInputWithTables, Dict [str, Any]], str] Examples using create_sql_query_chain ¶ Build a Question/Answering system over SQL data 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 Nov 14, 2023 · LangChain SQL - Agent Setup. Additionally, it integrates with Langsmith for tracing and feedback collection. 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. 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. Must provide exactly one of ‘toolkit’ or SQL One of the most common types of databases that we can build Q&A systems for are SQL databases. Follow these installation steps to create Chinook. Task Solving with Langchain Provided Tools as Functions - View Notebook RAG: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - View Notebook An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. tools. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. 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. It is then wrapper into ToolNode to make it a node with fallback. - tryAGI/LangChain 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. env. g. Feb 13, 2024 · Plan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. Aug 21, 2023 · Query Generation: LangChain translates user intent into structured SQL queries that precisely fetch relevant data. toolkit. nkuxc poelc ipzj qgu yhjarzd bcaek dkk wbt bgko tmnui