Langchain sql agent examples. guide/2-sample-databases-sqlite/, placing the .

Langchain sql agent examples. 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. """ import contextlib from tempfile import TemporaryFile from dotenv import load_dotenv from langchain. Dec 9, 2024 · Construct a SQL agent from an LLM and toolkit or database. Mar 7, 2025 · 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 Reading an SQL database can be challenging for humans. In this step-by-step tutorial, we will show […] SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Sep 7, 2024 · Technologies and Tools Azure Cognitive Services: Facilitates document retrieval for financial analysis. Must provide exactly one of ‘toolkit’ or 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. Get started Familiarize yourself with LangChain's open-source components by building simple applications. Toolkits are supported 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. Azure OpenAI GPT-4 for intelligent language understanding and generation of SQL queries in PostgreSQL. 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 . For comprehensive descriptions of every class and function see the API Reference. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. Specifically, check the equality operator (==) used for llm and correct it to a single =. How-to guides Here you’ll find answers to “How do I…. SQLDatabaseToolkit # class langchain_community. These systems will allow us to ask a question about the data in a database and get back a natural language answer. extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. It is particularly useful in handling structured data, i. LangChain Framework: Powers the agent architecture, allowing seamless integration of RAG and This template enables a user to interact with a SQL database using natural language. Jun 15, 2023 · 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. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. These applications use a technique known as Retrieval Augmented Generation, or RAG. Note that, as this agent is in active development, all answers might not be correct. These guides are goal-oriented and concrete; they're meant to help you complete a specific task. If you'd prefer, you can follow the instructions to save the file locally as Chinook. 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. Dec 4, 2023 · In my previous blog, we explored the Langchain tool and its remarkable create_sql_agent function, which enables the creation of a powerful… Sep 28, 2023 · Usually it is an iterative process until the Agent reaches the Final Answer or output. 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. 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. They enable use cases such as: Generating queries that will be run based on natural language questions, Creating This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. """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. Setup: Install langchain-community. guide/2-sample-databases-sqlite/, placing the . If agent_type is “tool-calling” then llm is expected to support tool calling. Here's a quick example of how create_sql_agent # langchain_community. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). 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 Mar 11, 2024 · Unlock the full potential of database interactions with our guide on Natural Language to SQL using LangChain and LLM. 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. AutoGen for coordinating AI agents in collaborative workflows. We also released the langchain-sqlserver package, enabling the management of SQL Server as a Vectorstore in LangChain. e. Follow these installation steps to create Chinook. I followed this example: https://python. Toolkit is created using ‘db’ and Feb 19, 2024 · LangChain is an open-source framework for creating applications that use and are powered by language models (LLM/MLM/SML). Parameters llm (BaseLanguageModel) – Language model to use for the agent. Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. 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. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. Here's a quick example of how Apr 24, 2023 · Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. 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. 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. An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. Build resilient language agents as graphs. For conceptual explanations see the Conceptual guide. Must provide exactly one of ‘toolkit’ or Contribute to langchain-ai/langsmith-cookbook development by creating an account on GitHub. g. 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). 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 The SQL Agent provided by LangChain is a tool that allows you to interact with SQL databases using natural language. The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. Most SQL databases make it easy to load a CSV file in as a table (DuckDB, SQLite, etc. agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. agents. Jun 24, 2024 · LangChain offers an SQL Agent that allows for more flexible interactions with SQL databases. We hope to continue developing different toolkits that can enable agents to do amazing feats. - tryAGI/LangChain About Langchain SQL agent example to talk to postgresql database in natural language This notebook showcases an agent designed to interact with a SQL databases. This agent is valuable for building natural language interfaces to databases. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. ). 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. toolkit. 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. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by 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 "Plot bar chart" now for 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. May 7, 2024 · Here we use our SQL Agent that will directly run queries on your MySQL database and get the required data. 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. Additionally, it integrates with Langsmith for tracing and feedback collection. sql Test SELECT * FROM Artist LIMIT 10; Now, Chinhook. Contribute to langchain-ai/langgraph development by creating an account on GitHub. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. Azure Database for PostgreSQL for data storage and querying. 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. Setup First, install the required packages and set your environment variables. db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. Providing the model with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. sql file and create an in-memory SQLite database. Apr 2, 2025 · You can expose SQL or Python functions in Unity Catalog as tools for your LangChain agent. base. This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions. , data incorporating relations among entities and variables. Aug 26, 2023 · 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 MLflow's LangChain Integration streamlines the process of developing and operating modern compound ML systems. The language model used is OpenAIs GPT-4o mini. Apr 24, 2023 · Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. It can understand natural language questions, convert them into SQL queries, execute the queries, and present the results in a user-friendly format. The application showcases a shipping company Dec 1, 2024 · How to build an AI-Powered SQL Data Analysis Agent with LangChain and CrewAI Tutorials New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. Tools are essentially functions that extend the agent’s capabilities by C# implementation of LangChain. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. Setup Feb 10, 2025 · This guide explains the fundamentals of AI agents and shows you how to build them using n8n, with practical examples for software developers. This guide covers how to prompt a chat model with example inputs and outputs. db file in a notebooks folder at the root of this repository. ?” types of questions. 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. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. This example will use OpenAI as the LLM. agents Apr 24, 2023 · Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. db"). 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. 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. For full guidance on creating Unity Catalog functions and using them in LangChain, see the Databricks UC Toolkit documentation. read Chinook_Sqlite. sql In this tutorial, we will learn how to chat with a MySQL (or SQLite) database using Python and LangChain. 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. llms import OpenAI, SQLDatabase db = SQLDatabase() db_chain = SQLDatabaseChain. llm: BaseLanguageModel The language model (for use with QuerySQLCheckerTool) Instantiate: Apr 9, 2024 · The docs describe how to create an SQL agent using OpenAI as an example but implying that the approach is generic. Dec 9, 2024 · Key init args: db: SQLDatabase The SQL database. sql Chinook Database for SQLite: Chinook_Sqlite. com/docs/use_cases/qa_structured/sql#extending-the-sql-toolkit However, I want to use VertexAI instead of the OpenAI ones. 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. We will cover: Appending a "query validator" step to the query generation; Prompt engineering to reduce the incidence of errors. LangChain comes with a built-in chain for this: create_sql_query_chain. I am able to use create_sql_query_chain just fine against either an OpenAI LLM or . LangChain and LangGraph SQL agents example. LangChain is an excellent framework equipped with components and third-party integrations for developing applications that leverage LLMs Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. We'll largely focus on methods for getting relevant database-specific information in your prompt. Contribute to johnsnowdies/langchain-sql-agent-example development by creating an account on GitHub. sql Run sqlite3 Chinook. In this guide we'll go over the basic ways to create a Q&A system over tabular data in databases. It can recover from errors by running a generated query This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. 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. 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. db in the same directory as this notebook: Save this file as Chinook_Sqlite. Here are some relevant links: Feb 22, 2024 · Introduction # :bulb: Quick Links: Chinook Database for MySQL: Chinook_MySql. We will also demonstrate how to use few-shot prompting in this context to improve performance. langchain. Below we will use the requests library to pull the . Getting Started SQL One of the most common types of databases that we can build Q&A systems for are SQL databases. 🌐 SQL Database - Databricks SQL is integrated with SQLDatabase in LangChain, allowing you to access the auto-optimizing, exceptionally performant data warehouse. You are an agent designed to interact with a SQL database. Note that this approach is lightweight, but ephemeral and not thread-safe. The… 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 5, 2023 · For example, the standard SQL Toolkit draws from standard best practices that have been extensively covered in this blogpost. In this guide we'll go over some strategies for validating our queries and handling invalid queries. In this guide we'll go over prompting strategies to improve SQL query generation. agents import AgentExecutor from langchain. For end-to-end walkthroughs see Tutorials. Installation How to: install SQLDatabaseToolkit # class langchain_community. 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). I changed the example only on 2 places: embeddings = HuggingFaceEmbeddings( Example from langchain_experimental. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. 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. Sep 11, 2023 · Depending on the user input, the agent can then decide which, if any, of these tools to call. Once you've done this you can use all of the chain and agent-creating techniques outlined in the SQL use case guide. SQLDatabaseToolkit [source] # Bases: BaseToolkit SQLDatabaseToolkit for interacting with SQL databases. format_scratchpad import format_log_to_str from langchain. These are applications that can answer questions about specific source information. from_uri("sqlite:///Chinook. (Update when i a Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. Here's a quick example of how 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. Aug 19, 2023 · Introduction Natural language querying allows users to interact with databases more intuitively and efficiently. sql import SQLDatabaseChain from langchain_community. db and instantiate the database via db = SQLDatabase. To set up this agent, we use the create_sql_agent function, which includes the SQLDatabaseToolkit. Refer to AI Agent for more information on the AI Agent node itself. Execute SQL query: Execute the SQL query. 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 Nov 21, 2023 · 0 I want to use sql Agent together with few shot examples. > Entering new AgentExecutor chain Action: list_tables_sql_db Action Input: "" . sql. However, with accurate prompts, Gemini models can generate answers based on the data. Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. Ready to support ollama. 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). We try to be as close to the original as possible in terms of abstractions, but are open to new entities. We will cover implementations using both chains and agents. Oct 1, 2024 · In this tutorial, you will learn how to create a message history and a UI for a LangChain chatbot application. db Run . For this, four datasets from the European Statistical Office (Eurostat) are loaded Apr 7, 2024 · Deploying agents with Langchain is a straightforward process, though it is primarily optimized for integration with OpenAI’s API. To set it up follow the instructions on https://database. Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Build an Extraction Chain In this tutorial, we will use tool-calling features of chat models to extract structured information from unstructured text. Through the use of the Gemini API, you will be able retrieve necessary information by chatting with a SQL database. Be careful running it on sensitive data! This uses the example Chinook database. note The SQL Agent uses a SQL database as a data source. The below example will use a SQLite connection with Chinook database. agent_toolkits. tapjvmd ycfep ywsu qrm urrg qiyoi iyzz olfkjvu gogdyzaz uoymmt