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Langchain sql agent github. Toolkit: Uses SQLDatabaseToolkit for database interactions.
Langchain sql agent github. The agent is integrated with a set of tools, such as an SQL tool, and utilizes a memory buffer to maintain conversation history across sessions. Jul 11, 2024 · Description I'm trying to make an SQL agent with hugging face llm but it seems like the agent settings are only supposed to work with openai. g. py: Simple app using StreamlitChatMessageHistory for LLM conversation memory (View the app) mrkl_demo. agent. The idea is that we use RAG to fetch relevant DB table info and make the SQL agent job easier in finding the right table as MixQ/At is a Q&A bot powered by Mixtral-8x7b to interact with SQLite databases. Toolkit: Uses SQLDatabaseToolkit for database interactions. First, let us see the current SOTA text to sql workflow: Schema and Metadata Extraction: The system processes the provided database (e. ts and "prompt" with your request: Nov 14, 2023 · LangChain SQL - Agent Setup. []. In Build resilient language agents as graphs. I will be using django rest framework for Post and get requests when a user query a search and the LangChain agents will retrieve knowledge from the database. We try to be as close to the original as possible in terms of abstractions, but are open to new entities. Get started with the langchain_sqlserver library with the following tutorials. create_sql_agent (llm [, ]) Construct a SQL agent from an LLM and toolkit or database. This project provides a Python package, langchain-looker-agent, that allows you to build LangChain agents capable of interacting with your Looker data via its Avatica JDBC driver. Update agent_config. Could you please suggest me , how to improve the performance of the api call using langchain agents to get the sql results fastly. base. ts - Basic SQL query generation using generate_sql_query function agent. Toolkit is created using ‘db’ and 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. This project is a Streamlit-based web application that allows users to interact with SQL databases (SQLite or MySQL) using the LangChain framework and Oct 27, 2024 · LangChain SQL Agent This project is a system that connects to a PostgreSQL database and runs AI-powered SQL queries. Contribute to padmaDS/Langchain-sql-agent development by creating an account on GitHub. This guide provides detailed steps to create a SQL agent that leverages Langchain, Composio, and ChatGPT to execute SQL queries, document them and plot graphs based on the data. In this guide we'll go over the basic ways to create a Q&A system over tabular data Dive into the world of conversational data exploration with SQLChat! 🚀 This project empowers you to interact with your SQL databases using natural language, thanks to the magic of LangChain, open-source LLMs (via Groq), and Streamlit. Oct 27, 2023 · langchain. format_scratchpad import format_log_to_str from langchain. The agents leverage a language model to interpret user queries, translate them into SQL statements, execute these statements against a database, and present the results. Full details and video recording available here: RAG on Azure SQL Server. Azure Database for PostgreSQL for data storage and querying. Jan 3, 2024 · I am trying to build a langchain SQL database agent where I want to query only one view for now. Natural language querying allows users to interact with databases more intuitively and efficiently. MPT, from MosaicML) to query Databricks SQL. Aug 18, 2024 · Checked other resources I added a very descriptive title to this question. The project includes two example scripts: Mar 13, 2024 · Checked other resources I added a very descriptive title to this question. This tool extracts the table names and database metadata given the context, but the generated query isn’t always 这是一个基于 LangChain 和 DeepSeek 大语言模型构建的 SQL 智能代理系统，通过 Gradio 提供用户友好的界面 Jul 12, 2025 · LangChain SQL Agent Tutorial 2025. This repository provides tools and examples for leveraging advanced language processing capabilities to interact with and query SQL databases effectively. Ensure you have Python 3. It integrates advanced guardrails for enhanced security, providing a safe, efficient way to query and manage data through intuitive language commands. It uses LangChain and FAISS to convert database content into vector format and generate SQL queries based on natural language questions. """ import contextlib from tempfile import TemporaryFile from dotenv import load_dotenv from langchain. 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. sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. GitHub Gist: instantly share code, notes, and snippets. ts - Agent-based SQL querying with formatted output examples_of_langchain_db_llm - Advanced graph-based query processing examples sql_agent. Sep 21, 2023 · To add memory to the SQL agent in LangChain, you can use the save_context method of the ConversationBufferMemory class. Contribute to sugarforever/LangChain-Tutorials 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. The key frameworks used in this project include OpenAI, LangChain Example application for constructing and running an LLM-based LangChain SQL Agent based on GPT-4o mini that can dynamically query a database and invoke multiple visualization tools - EliasK93/LangC Jan 19, 2024 · As for the second part of your question, I wasn't able to find specific information on how the SQL agent in LangChain handles database connections, specifically for Oracle Database, within the repository. - tryAGI/LangChain Feb 7, 2024 · It seems like the create_sql_agent function in LangChain is indeed ignoring the custom prefix and suffix when the agent_type is set to "zero-shot-react-description". agents Jul 12, 2025 · Files file. The SQLDatabaseToolkit toolkit requires: a LLM or chat model (for instantiating the QuerySQLCheckerTool tool). Implemented schema-aware prompts and conversational context handling for complex query generation. When use_query_checker is set to True, the query_checker_chain is used to validate and potentially modify the initial SQL command generated by the LLM. A more efficient solution would be to enhance the SQL agent to support session and role management, but that would require changes to the LangChain JS framework itself. LangChain offers SQL Chains and Agents to build and run SQL queries based on natural language prompts. The Medium_LangChain_Demo. May 28, 2024 · To fine-tune an open-source LLM like LLaMA 3 to a specific LangChain agent format, such as LangChain's create_sql_agent, you need to follow these steps: Prepare the Dataset: The dataset should include examples of inputs and expected outputs that the agent should handle. The application interacts with a PostgreSQL database to retrieve and process data. Additionally, it integrates with Langsmith for tracing and feedback collection. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields. Session Management Mar 26, 2025 · The end user asks simple questions in English, and the system should create an SQL query based on the table names and column names in the database, which is MSSQL. Essentially, langchain makes it easier to build chatbots for your own data and "personal assistant" bots that respond to natural language. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. AutoGen for coordinating AI agents in collaborative workflows. Apr 9, 2024 · The docs describe how to create an SQL agent using OpenAI as an example but implying that the approach is generic. Users can ask natural language questions, which the system translates into SQL queries, executes against a SQLite database, and then provides detailed answers based on the query results. You have access to a Microsoft SQL Server database. ''' FORMAT_INSTRUCTIONS = """Please use the sql_agent. I used the GitHub search to find a similar question and This repository demonstrates how to use a LangChain SQL agent to query Google Cloud BigQuery using the Gemini Generative AI through Vertex AI. Mar 27, 2024 · The current structure of the SQL agent in the LangChain codebase involves creating a SQL agent from a language model (LLM) and a toolkit or database. py: An agent that replicates the MRKL demo (View the app) minimal_agent. 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. 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. 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. Jul 5, 2023 · Natural language querying allows users to interact with databases more intuitively and efficiently. LangChain / LangGraph SQL Agent Demo This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. I am able to use create_sql_query_chain just fine against either an OpenAI LLM or Azure SQL DB, Langchain, LangGraph and Chainlit Sample RAG pattern using Azure SQL DB, Langchain and Chainlit as demonstrated in the #RAGHack conference. Nov 23, 2023 · agent is defined as follows: `toolkit = SQLDatabaseToolkit (db=db, llm=llm) toolkit. But also I would like to use CromaDB to save embeddings with local knowledge of the database structure, columns content, So this means thy I wou This repository demonstrates how to build a multi-agent AI system using: LangChain for natural language to SQL translation. Oct 31, 2024 · We followed the LangChain tutorial to query our Azure SQL database using LangChain and OpenAI through a SQL Agent. After you clone the repo, follow these instructions: openAI. This project integrates LangChain with a MySQL database to enable conversational interactions with the database. 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. This README provides a step-by-step guide on how to set up and use the SQL Database Agent. Contribute to langchain-ai/langgraph development by creating an account on GitHub. The agent is powered by Azure OpenAI's GPT model and is configured to interact with a SQLite database of the Chinook digital media store. This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions. while executing the above api call, its taking more time for query generation and execution. py: Simple streaming app with langchain. The agent was made with LangChain. Be sure that the tables actually exist by calling sql_db_list_tables first! For demonstration purposes, we will access a prompt in the LangChain Hub. LangChain is an excellent framework equipped with components and third-party integrations for developing applications that leverage LLMs Oct 1, 2023 · How to build a LangChain agents that can interact with data from a postgresql database of an HR systems. ') generated by sql_db_query_checker and sql_db_query is due to the use of the query checker tool. LangChain SQL Agent provides a more flexible way of interacting 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. Jan 23, 2024 · Based on your requirements, it seems like you want to modify the create_sql_agent function to return only the SQL query to reduce the latency caused by high completion tokens when generating HTML tables and summaries with LLM (GPT-4). Must provide exactly one of ‘toolkit’ or agent_executor_kwargs (Optional[Dict[str, Any]]) – Arbitrary additional AgentExecutor args. py) or using LangGraph (app-langgraph. It is particularly focused on financial data analysis, offering insights into how natural language processing (NLP) can be utilized to simplify data querying processes. It allows the user to be able to interact by himself with the database. 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. - langchain-crashcourse/sql-agent-notebook. I used the GitHub search to find a similar question and Tutorial to LangChain SQL Agent This repo contains code snippets and datasets used in my Medium article "A Beginners Guide to LLM Agents and Toolkits". get_tools () PREFIX = '''You are a SQL expert. About Built a natural language chatbot interface for SQL databases using LangChain Toolkit and Agents. The goal of this repo is to provide users the ability to use Amazon Bedrock and generative AI to take natural language questions, and transform them into relational database queries against MSSQL Databases using LangChain SQL Agent. Ask questions in plain English and get instant answers from your data! 🤖 Currently, SQLChat focuses on SELECT queries, enabling you to retrieve and analyze 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. Jul 12, 2024 · Checked other resources I added a very descriptive title to this question. py) to define the RAG process. All the tutorials works with Azure SQL or SQL Server 2025. This project demonstrates the integration of LangChain's SQL utilities and ChatGroq, a language model, to create an intelligent agent that can generate and execute SQL queries based on natural language user inputs. It highlights the use of SQL agents to efficiently query large databases. This allows you to interact with your BigQuery data using natural language, leveraging the power of Gemini's advanced language understanding capabilities. I used the GitHub search to find a similar question and This repository demonstrates how to build a conversational SQL Query Assistant using LangChain's create_sql_agent. LLMs make it possible to interact with SQL databases using natural langugae. It leverages the power of OpenAI's GPT models and LangChain's memory and prompt engineering capabilities to optimize and execute queries, making it a robust tool I am using LangChain SQL Agent to query a database, but I am encountering challenges in handling ambiguous column names and missing values when users ask questions. . For this, four datasets from the European Statistical Office (Eurostat) are loaded Nov 25, 2023 · I would like to use SQL Agent to query database. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs) like GPT, we have created an application that enable users to query databases using NLP LangChain Looker SQL Agent Connect LangChain to your Looker instance for conversational data querying using Looker's Open SQL Interface and its governed semantic layer. 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 . ChatOpenAI (View the app) basic_memory. Integrated Ollama for language understanding and Flask for the web UI, reducing data analysis effort by 50% for non-technical users. You can achieve this by creating a new function that wraps around the create_sql_agent function. I'm trying to convert this sql agent to gemini llm and BigQuery but in the following step I'm receiving an error: query_check_system = """You are a SQL expert with a strong attention to detail. Jul 8, 2024 · Description we are trying to create oracle chatbot using langchain and SQLAlchemy. The dataset format should be This project is designed to create and configure a ReAct (Reasoning and Acting) agent using LangChain and OpenAI's GPT-4o model. A Streamlit app that allows users to query SQLite or MySQL databases using LangChain agents powered by Groq's LLM for natural language processing. A sample application demonstrates the usage of Langchain and SQL Agent - trguduru/langchain-sql-agent LangChain_SQL_Agent SQL Database Agent Overview The SQL Database Agent is a tool designed to interact with SQL databases using natural language queries. Langchain_SQL_Agent Enterprise data is often stored in SQL databases. 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. agents. We will also require langgraph to demonstrate the use of the toolkit with an agent. sql. ipynb notebook is designed for AI architects and ML engineers interested in exploring the capabilities of LangChain in conjunction with SQL databases. 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. 🚀 An intelligent AI-powered SQL agent that allows users to interact with a PostgreSQL database using natural language queries. Contribute to TheAILearner/Langchain-Agents development by creating an account on GitHub. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. yaml with the Databricks Resources (warehouse, hostname, catalog, schema), model resources (model endpoints, temperature, max tokens, etc. Azure OpenAI GPT-4 for intelligent language understanding and generation of SQL queries in PostgreSQL. This makes data retrieval from the database faster and more accurate. Langchain Agents. I searched the LangChain documentation with the integrated search. Specifically, check the equality operator (==) used for llm and correct it to a single =. After setting up the environment and installing 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 This repository contains reference implementations of various LangChain agents as Streamlit apps including: basic_streaming. The sample is build using plain LangChain (app. """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. 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. Learn to use LangChain's SQL Database Chain and Agent with large language models to perform natural language queries (NLQ) of Amazon RDS for Jun 28, 2024 · Hello, thanks for this amazing explanation. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs) like Jun 12, 2024 · Checked other resources I added a very descriptive title to this question. Parameters llm (BaseLanguageModel) – Language model to use for the agent. This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. ' vs '$. extra_tools (Sequence[BaseTool]) – Additional tools to give to agent on top of the ones that come with SQLDatabaseToolkit. The goal is to enable the agent to process user queries, interact with an SQL database, and return coherent, context-aware C# implementation of LangChain. I used the GitHub search to find a similar question and README langchain-sql-databricks TLDR; this repo contains some starter examples for working with Langchain and LLM Instruction models (e. agents import AgentExecutor from langchain. chat_models. The LangChain agents will interact with data from the database when queried. Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. 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). The project includes a custom Python script for extended functionality, integration with the Gemini API for advanced NLP tasks, a Jupyter notebook guide Oct 29, 2024 · 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. ipynb at main · bhattbhavesh91/langchain-crashcourse Source code for the upcoming blog post, Generative AI for Analytics: Performing Natural Language Queries on Amazon RDS using SageMaker, LangChain, and LLMs. create_sql_agent / SQLDatabaseToolkit - Agent never gets DB schema and tries to query nonexistent table names. Identify which tables can be used to answer the user's question and write and execute a SQL query accordingly. If agent_type is “tool-calling” then llm is expected to support tool calling. Once the code is stabilized, register the model, run evaluation, register and deploy. #12458 Jan 20, 2025 · LangChain + OpenAI + Azure SQL. The LangChain Crash Course Repository is a concise and comprehensive collection of learning materials for the LangChain programming language. Built using LangChain, OpenAI/Groq LLMs, and Streamlit, this AI-agent can generate, execute, and refine SQL queries dynamically while providing explanations for the This project demonstrates how to use LangChain to build agents that can process natural language queries and interact with SQL databases. If you're just getting started with LangChain, take a look at this article with the related sample: LangChain Integration for Vector Support for SQL-based AI Aug 22, 2023 · HI! I recently started using Langchain to create some sort of an assistant for my user to answer any questions related to their data in my database. To run simply run the bash script below and replace "examplePath" with the relative path from src/index. Mar 24, 2024 · Checked other resources I added a very descriptive title to this question. I used the GitHub search to find a similar question and 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. Overview Langchain SQL Agent is a project designed to bridge the gap between language models and SQL databases. I've tried too many agents changing the whole toolkits and agent types still I get some errors regarding unexpected argument was passed. For create_sql_agent, this would typically involve SQL queries and the corresponding responses. This project demonstrates how to use LangChain to build an AI agent that can query the Chinook database using SQL. 8 or higher installed. The user will ask something general like "How ma This project integrates LangChain ,SQLAlchemy, and OpenAI LLM to create a custom agent capable of interacting with local databases. py: A create_sql_agent # langchain_community. My current approach involves using the SQL agent with Langraph. I used the GitHub search to find a similar question and This project demonstrates how to build an interactive SQL query system using LangChain, GPT-4, and a SQLite database. Agent Setup LangChain Agent: Created with create_sql_agent, which handles the logic of converting natural language into SQL queries. , SQLite or CSV This project demonstrates how to build an agentic system using Large Language Models (LLMs) that can interact with multiple databases and utilize various tools. The application showcases a shipping company Contribute to langchain-ai/langsmith-cookbook development by creating an account on GitHub. The language model used is OpenAIs GPT-4o mini. It leverages natural language processing (NLP) to query and manipulate database information using simple, conversational language. I have mentioned the view name in the System Prompt and I have passed view_support=True to the SQLDa Feb 14, 2024 · Checked other resources I added a very descriptive title to this question. The model interprets user queries and formulates SQL queries to interact with the database. The main function create_sql_agent is responsible for constructing this agent, and it can be customized with various parameters such as the toolkit or database to use, agent type, prompt Aug 2, 2024 · I am following the SQLAgent tutorial from Langgraph and adding RAG to it. If anyone knows how to fix it please help. 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. It features a Streamlit integration for an intuitive user Langchain Agents. The assistant connects to a PostgreSQL database and dynamically generates SQL queries based on natural language inputs. LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Sep 23, 2024 · The difference in the actual executed query ('$. ), and base prompt Run data_setup to create data tables for testing Review and customize 01_sql_react_agent as needed Test the agent code using 02_evaluate. This uses prompt templates to generate queries and show results from the queries executed via custom tools - MohakSriv/Langchain-SQL-Agent Langchain Agents. please find the below detail output, while calling the API [2024-07-08 22:18:54] select This project demonstrates how to build a powerful multimodal agent for document analysis using Docling for PDF extraction and LangChain for creating AI chains and agents. While it generally works fine, we've encountered an issue where the Large Language Model (LLM) truncates the expected answer. db (Optional[SQLDatabase]) – SQLDatabase from which to create a SQLDatabaseToolkit. agent_toolkits. This is not required to use the toolkit. Thanks System Info Langchain Agents. It showcases the seamless integration of tabular and textual data extracted from PDFs into a unified query system Dec 9, 2024 · Construct a SQL agent from an LLM and toolkit or database. kqaicpifdrhkwygtirqqdgbdfttugepfujzlmiscmrvolpj