Langchain create sql agent. Dec 9, 2024 · langchain_community.

Langchain create sql agent. This can be done by checking the result set and returning the appropriate message if no records are found. Callbacks in LangChain are designed to provide hooks for the execution of a chain. The first framework i used for this was Langchain. The query is then ran on your MySQL database using a built-in function. Query Execution: The above formulated and validated form of SQL is then executed on the database. agent_toolkits import SparkSQLToolkit, create_spark_sql_agent from langchain_community. Oct 27, 2023 · langchain. To reliably obtain SQL queries (absent markdown formatting and explanations or clarifications), we will make use of LangChain's structured output abstraction. We’ll enhance the chatbot’s ability to make SQL queries by giving it additional tools. create_sql_agent(llm: BaseLanguageModel, toolkit: SQLDatabaseToolkit, agent_type: AgentType = AgentType. from_uri() function in LangChain to read a SQLite database file and insert values from a dataframe into it, you can follow these steps: Create an SQLDatabase Instance: Use the from_uri class method to create an instance of SQLDatabase by providing the SQLite database URI. Quickstart In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. Toolkit is created using ‘db’ and This will help you get started with the SQL Database toolkit. 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. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the Mar 7, 2025 · SQL Generation & Validation: Regarding the data queries, the system will create, check and rectify the SQL. Other agents will be instantiated in more generic way as we will see below in other examples. For example, Initialization from langchain_community. agent_toolkits import SQLDatabaseToolkit This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. Must provide exactly one of ‘toolkit’ or Construct a SQL agent from an LLM and toolkit or database. agents. Let's select a chat model for our application: sql_agent. \nGiven an input question, create a This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. ⚠️ Security note ⚠️ Building Q&A Nov 14, 2023 · LangChain SQL - Agent Setup. Toolkit is created using ‘db’ and Aug 26, 2023 · 2 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 to sqlite like the following code: from langchain. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them: Memory in LLMChain Custom Agents In order to add a memory to an agent we are going to perform the following steps: We are going to create an LLMChain with memory. 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). What is LangChain? LangChain is a software framework designed to help create applications that utilize large language models (LLMs). 5L. 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 . Feb 14, 2024 · Description how do I get the output (quantity) from the create_sql_agent and pass it to my tool which expects the value to calculate it? I have a database, I want to be able to add the total quantity of a chemical (s): an example of the quantity is 2. note Feb 19, 2024 · LangChain is an open-source framework for creating applications that use and are powered by language models (LLM/MLM/SML). create_sql_agent / SQLDatabaseToolkit - Agent never gets DB schema and tries to query nonexistent table names. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. Apr 2, 2025 · Learn about the LangChain integrations that facilitate the development and deployment of large language models (LLMs) on Azure Databricks. Jun 20, 2024 · I am using the above code to create sql agent, the code runs, it generates reasonable sql queries, but the query results were all hallucinated, not the actual result based on the database. 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. SQLDatabaseToolkit # class langchain_community. Aug 2, 2024 · I'm trying to do sql retrieval using a langchain sql agent, pretty much as done in the following snippet: repo_id="meta-llama/Meta-Llama-3-8B-Instruct", task="text-generation", max_new_tokens=512, do_sample=False, repetition_penalty=1. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. LangChain Python API Reference langchain-community: 0. This powerful combination allows for intuitive interaction with databases without requiring direct SQL knowledge. agents import AgentType, tool, create_sql_agent @tool def my_first_awesome_tool(human_message: str) -> list: """ Searches for my Feb 9, 2024 · Overview In this project, we’re going to create a chatbot that can talk to the RappelConso database through the Langchain framework. Feb 7, 2024 · I have used Langchain - create_sql_agent to generate SQL queries with a database and get the output result of the generated SQL query. \nGiven an input question, create a syntactically correct Spark SQL langchain. Sep 10, 2024 · The core part of the tutorial involves creating the SQL Agent using Langchain’s create_sql_agent module. LangChain Framework: Powers the agent architecture, allowing seamless integration of RAG and 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. For simplicity we'll use the same dataset for final response and trajectory evaluation, so we'll add both ground-truth responses and trajectories for each example question. sql. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. If you don’t like to go thru whole article just May 16, 2024 · Convert question into a simple query from langchain. 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. \nGiven an input question, create a syntactically correct Spark SQL query to run, then look at Dec 9, 2024 · langchain_community. If agent_type is “tool-calling” then llm is expected to support tool calling. The agent is equipped with toolkit to connect to your SQL database and read both the metadata and Apr 2, 2025 · Interactively query your data using natural language with the Spark DataFrame Agent or Databricks SQL Agent. sql_database import SQLDatabase from langchain. Setting up BigQuery and OpenAI Environment SqlAlchemy and Model Connection Define SQL Agent Streamlit UI First, let's create a dataset that evaluates end-to-end performance of the agent. create_sql_agent (llm [, ]) Construct a SQL agent from an LLM and toolkit or database. 3. We will cover: Appending a "query validator" step to the query generation; Prompt engineering to reduce the incidence of errors. Here is how my code looks like, it is working pretty well. agent_toolkits import SQLDatabaseToolkit from langchain. Setup: Install langchain-community. 27 agent_toolkits create_sql_agent Convert question to SQL query The first step is to take the user input and convert it to a SQL query. SQLDatabaseToolkit [source] # Bases: BaseToolkit SQLDatabaseToolkit for 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. 01. In this tutorial, we will walk through how to build an agent that can answer questions about a SQL database. Note that, as this agent is in active development, all answers might not be correct. Aug 18, 2024 · I'm attempting to stream the final answer from the agent_executor, but the output currently includes all the AI-generated responses, such as the SQL queries, in addition to the final answer. I am able to use create_sql_query_chain just fine against either an OpenAI LLM or an Ollama LLM (examples below). This method uses toolkit instead of simple list of tools. SQL In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. LangChain's strength lies in its wide array of integrations and capabilities. How to do Text-to-SQL in LangChain? Sep 28, 2023 · Now initialize the create_sql_agent which is designed to interact with SQL Database as below. Build resilient language agents as graphs. Something like: from langchain. GitHub Gist: instantly share code, notes, and snippets. chat_models import ChatOpenAI from langchain. In this guide we’ll go over the basic ways to create a Q&A system over tabular Apr 7, 2024 · from langchain_community. You can read more about them in the documentation. 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. create_spark_sql_agent # langchain_community. In this guide we'll go over some strategies for validating our queries and handling invalid queries. base. To set it up, follow these instructions, placing the . It will also have a memory feature to remember past interactions with 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. ⚠️ Security note ⚠️ Sep 22, 2023 · Use the combination of the prefix variable and the tool function description. At a high level, the agent will: Building Q&A systems of SQL databases requires executing model-generated SQL queries. Toolkit is created using ‘db’ and 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 You are an agent designed to interact with a SQL database. sql import SparkSession spark = SparkSession. Toolkit is created using ‘db’ and 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 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 . There are inherent risks in doing this. The agent can translate natural language queries coming from users into SQL, and execute them against a database. utilities. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Aug 17, 2023 · I'm creating a feature that uses 'LangChain' to turn user input questions into SQL. It's important to ensure that your SQL query is correctly formatted and adheres to the SQL dialect supported by your database. The function create_sql_agent you've used in your code is designed to construct a SQL agent Mar 13, 2024 · How's everything going on your end? To use the SQLDatabase. May 7, 2024 · The SQL Agent from LangChain is pretty amazing. LangChain / LangGraph SQL Agent Demo This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. This is often achieved via tool-calling. Whereas in the latter it is common to generate text that can be searched against a vector database, the approach for structured data is often for the LLM to write and execute queries in a DSL, such as SQL. We are going to use that LLMChain to create 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. #12458 Aug 15, 2024 · Chain和Agent查询数据库 在LangChain，其提供了SQL Chain链和SQL Agent代理，他们支持基于自然语言提示构建和运行SQL查询，以此来操作数据库，并且与SQLAlchemy支持的任何SQL版本兼容。（例如， MySQL，PostgreSQL，Oracle SQL，Databricks，SQLite）。 可以用来完成以下任务： 基于自然语言问题生成能运行的SQL查询 创建 Jul 11, 2023 · Everything about SQL Agent LangChain and how to do database querying using natural language for easier interaction. Memory in Agent This notebook goes over adding memory to an Agent. It can recover from errors by running a generated query, catching the traceback and regenerating it Dec 9, 2024 · Construct a SQL agent from an LLM and toolkit or database. So the SQL Agent starts off by taking your question and then it asks the LLM to create an SQL query based on your question. Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. This was my first time writing an agent with a good and serious usecase. Working code. Today, we’ll explore how to create a sophisticated SQL agent… Jan 18, 2025 · Sweet and simple GenAI SQL Agent using LangChain, allowing to Chat with your Database. I searched the LangChain documentation with the integrated search. Feb 23, 2024 · I want to use the memory in sql agent and need some assistance here. agent_toolkits import create_sql_agent agent_executor = create_sql_agent (llm, db = db, agent_type ="openai-tools", verbose =True) Apr 9, 2024 · The docs describe how to create an SQL agent using OpenAI as an example but implying that the approach is generic. We will cover: How the dialect of the LangChain SQLDatabase impacts the prompt of the chain; How to format schema information into Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. run but I would like to get the various observations and graph the results. Dec 13, 2024 · In a future post, we’ll explore the evolution of LangChain’s agent design, moving beyond the now-deprecated create_sql_agent and create_react_agent approaches. TheAILearner explains how to import necessary modules, set up the agent executor, and utilize the agent type for creating SQL queries. Setup This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. Feb 22, 2024 · Checked other resources I added a very descriptive title to this issue. Very easy to implement: Its pretty convenient to import LLMs Gpt, Claude, Gemini. 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 SQL Agent with just a few lines of code. toolkit. db file in the directory where your code lives. Jul 12, 2024 · I am trying to create_sql_agent to create an agent that takes NL query and provide answer to it using information the connected database. spark_sql. Be sure that the tables actually exist by calling sql_db_list_tables first! Sep 7, 2024 · Technologies and Tools Azure Cognitive Services: Facilitates document retrieval for financial analysis. builder. I used the GitHub search to find a similar question and di Feb 22, 2024 · Checked other resources I added a very descriptive title to this issue. getOrCreate() schema = "langchain_example" Feb 19, 2024 · What's cooking in your code kitchen today? Yes, it is indeed possible to create an SQL agent for making queries on Google BigQuery using the latest version of LangChain. In this tutorial we Feb 19, 2024 · SQL Agent for Google Big Query🤖 Hey @hugoferrero! Great to see you back here, diving into the possibilities with LangChain and Google BigQuery. Sep 28, 2023 · Usually it is an iterative process until the Agent reaches the Final Answer or output. 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. 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. prompts import PromptTemplate template = '''Answer the following questions as best you can. Setup This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take. I know that 'create_sql_agent' has logic to fix any errors in the query answered by 'openai'. To set up this agent, we use the create_sql_agent function, which includes the SQLDatabaseToolkit. This article provides a step-by-step guide. Dec 9, 2024 · In the world of AI and data analysis, the ability to interact with databases using natural language is becoming increasingly valuable. If you want to get automated tracing from runs of individual tools Apr 21, 2024 · I am trying my best to introduce memory to the sql agent (by memory I mean that it can remember past interactions with the user and have it in context), but so far I am not succeeding. This chatbot will be able to understand natural language and use it to create and run SQL queries. 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. Additionally, it integrates with Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. But when I am using the above code I am getting invalid response with message invalid or incomplete response, if i don't give agent_type then I am getting error as invalid format missing Action after Thought. 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. Parameters: llm (BaseLanguageModel) – Language model to use for the agent. May 23, 2024 · To ensure that the create_sql_agent function returns "No record found" when no rows are selected, you need to modify the function to handle the case where the query result is empty. extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. Parameters llm (BaseLanguageModel) – Language model to use for the agent. create_spark_sql_agent( llm: BaseLanguageModel, toolkit: SparkSQLToolkit, callback_manager: BaseCallbackManager | None = None, callbacks: Callbacks = None, prefix: str = 'You are an agent designed to interact with Spark SQL. 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. Here is a modified version of the create_sql_agent function that includes this logic: Aug 19, 2023 · By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs) like ChatGPT, we can create applications that enable users to query databases using natural language. Is 'SQLDatabaseChai This example shows how to load and use an agent with a SQL toolkit. agents import create_sql_agent from langchain. The create_sql_agent function is still supported and can be used to construct a SQL agent from a Language Model and a toolkit or database. May 23, 2023 · When creating a create_sql_agent() how do you get the prompt, thought, and observation? I know how to get the final answer which is just the response of the agent_executor. 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. Getting Started agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. chains import create_sql_query_chain #This chain generates SQL queries for the given database from langchain_openai import ChatOpenAI 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. You are an agent designed to interact with a SQL database. agent_toolkits. Jun 24, 2024 · LangChain offers an SQL Agent that allows for more flexible interactions with SQL databases. agent. Sep 5, 2024 · 本文介绍了如何利用Python、LangChain和GPT技术，从PDF文档中提取并结构化数据，进而通过SQLite数据库和LangChain SQL Agent进行复杂查询，实现数据的高效管理和分析。 If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields. This tutorial demonstrates how to create a SQL agent using Cohere and LangChain. my code looks like below agent_executor = create_sql_agent (llm, db=db, verbose=True, agent_executor_kwargs= { Sep 24, 2024 · Using create_sql_query_chain with some models generates incorrect SQL what am i doing wrong? Sep 5, 2023 · The create_sql_agent function in LangChain is designed to construct an SQL agent from a language model and tools. Sep 21, 2023 · To add memory to the SQL agent in LangChain, you can use the save_context method of the ConversationBufferMemory class. agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. For detailed documentation of all SQLDatabaseToolkit features and configurations head to the API reference. Instead, we must find ways to dynamically insert into the prompt Apr 30, 2024 · Steps Covered for SQL Q&A Tutorial: Create a VM and Import the Necessary Libraries. Today, let’s dive deeper into the inner workings of this agent. This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions. In this example you find where sql_code is defined or created in the tool run, then send it to the run manager. ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database. Construct an SQL agent from an LLM and tools. Contribute to langchain-ai/langgraph development by creating an account on GitHub. spark_sql import SparkSQL from langchain_openai import ChatOpenAI from pyspark. wondering how is the agent connected to db, since the agent arguments don't include db and why sql_db_query tool doesn't execute on the sql db. ## Use exactly the following format. from langchain_core. db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. Jan 23, 2024 · To achieve your goal of returning only the SQL query from the create_sql_agent function, you can use the LangChain's callback functionality. Construct a SQL agent from an LLM and toolkit or database. LangChain is an excellent framework equipped with components and third-party integrations for developing applications that leverage LLMs You are an agent designed to interact with a SQL database. Then still return the sql output like normal. Don't merge one step content with the next one: Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. . Apr 12, 2024 · However, based on the current version of LangChain you're using, there isn't a direct parameter in create_sql_agent to pass the schema explicitly to avoid the sql_db_schema invocation. In this guide we'll go over prompting strategies to improve SQL query generation. I used the GitHub search to find a similar question and di Jul 24, 2024 · 在LangChain，其提供了SQL Chain链和SQL Agent代理，他们支持基于自然语言提示构建和运行SQL查询，以此来操作数据库，并且与SQLAlchemy支持的任何SQL版本兼容。 I built an SQL Agent with Langchain - Here's my experience My agent writes queries to retrieve data from Sqlite Databases. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. We'll largely focus on methods for getting relevant database-specific information in your prompt. 03, temperature=0. On the surface, you’ll never understand how it works but there’s a lot going on behind the scenes. 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. create_spark_sql_agent(llm: BaseLanguageModel, toolkit: SparkSQLToolkit, callback_manager: Optional[BaseCallbackManager] = None, callbacks: Callbacks = None, prefix: str = 'You are an agent designed to interact with Spark SQL. You could also just append the sql code as a string/json to the output itself to return it in the typical agent view. yvzkc psoyc qhjghk awps lmrfh miwaosj nvarhvgm ccsavoe gxfws pom