Llm sql agent github. A Streamlit app that allows users to query SQLite or MySQL databases using LangChain agents powered by Groq's LLM for natural language processing. Contribute to defog-ai/sql-eval development by creating an account on GitHub. See our conceptual In this project, an Agentic AI Application has been built for running SQL Queries and interacting with Database in Python with Agno (previously known as Phidata), SQLTools and Llama4 LLM The agent's primary function is to generate accurate and efficient SQL queries to extract insights from the nba_roster database. It also saves the cost of About Developed a full-stack AI agent that translates natural language into executable SQL queries for a PostgreSQL database, using Python, Google's Gemini LLM, and Streamlit. Technically, it is a group chat with 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. InferenceClientModel allows you to call LLMs using Hugging Face's Inference API, either via Serverless or Dedicated endpoint, but SQLAgent 是一个 开源的（Open source）、大模型驱动的（LLM-Powered）、专注于私有化部署的Text2SQL 智能体（Agent） 项 SQLCoder is a family of large language models that outperforms gpt-4 and gpt-4-turbo for natural language to SQL generation tasks on our sql-eval framework, and significantly outperform all A lightweight, LLM-integrated SQL utility for intelligent, secure PostgreSQL querying. - asimadnan/LLM_SQL_agent. Streamlit based frontend to chat with your SQL db, the llm agent can run queries to answer questions. This project is a Streamlit-based web SQL Learning Support System with LLM. (VectorDB, GraphDB, RAG with LLM agents for SQL & graph databases. Whereas in the latter it is common to generate text that This project is an LLM-powered SQL Query Generator that allows users to generate SQL queries using natural language input. 5, the LangChain framework, and an Agentic RAG (Retrieval-Augmented Generation) pipeline to transform the This example demonstrates how to build and train a self-correcting SQL agent. The tool translates user langsmith-cookbook / testing-examples / agent-evals-with-langgraph / langgraph_sql_agent_eval. The This project demonstrates a simple yet powerful way to interact with SQL databases through a conversational interface. It receives the user's question and the database schema, then generates a SQL query AI-powered SQL chatbot using LangChain, Groq LLMs, and Streamlit. but that does't work in MS SQL database. ⚙️ Translate natural language into secure, production-ready SQL — built for real-world enterprise use. Contribute to EllianAbe/sql-agent development by creating an account on GitHub. py: Simple streaming app with A multi-agent system that transforms natural language questions into robust SQL queries, executes them, and returns structured answers. a NL2SQL). About LLM-Powered SQL Database Agents with LangGraph | Agentic AI The SQL Server Agent is a conversational AI Query CLI that enables you to interact with your SQL Server Database using natural language. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. we can do that, The above video shows how SQL LLM agent is interacting with sqlite DB This blog introduces an agent that communicates with SQL LLMCompiler is an Agent Architecture designed to speed up the execution of agent tasks by executing them quickly in the DAG. 5, the LangChain framework, and an Agentic RAG (Retrieval-Augmented Generation) pipeline to transform the This repository demonstrates a production-ready architecture for a multi-step Financial SQL Agent using cutting-edge LLM tooling. Easily query SQLite or MySQL databases in natural language. SQL Database Setup: Converts an Excel/CSV file into an SQLite database. This handbook corresponds to our survey paper [TKDE'2025]: 📖A LLM University: The code companion to the LLM University course containing a comprehensive list of modules. This repository contains reference implementations of various LangChain agents as Streamlit apps including: basic_streaming. Contribute to nofilamer/LLM-SQL-Agent-FASTAPI development by creating an account on GitHub. Whether you’re a data scientist, a developer, or a business analyst, this SQL agent can significantly streamline your database interactions and Contribute to git-ai-zyy/LLM-SQL-Agent development by creating an account on GitHub. It leverages LLM's (OpenAI, gpt-35-turbo-instruct) powerful language model to convert plain English questions Agent: Entry point for user questions, determining the user question's type and directing it to the relevant node. ai/oss agent bigquery charts sql postgresql bedrock business-intelligence openai spreadsheets vertex genbi text-to-sql rag text2sql duckdb llm anthropic Adopting an autonomous agent-based approach where a BigQuery SQL agent, equipped with an ODBC connection, iteratively attempts and refines SQL About The SQL Agent is a conversational AI tool designed to interpret natural language requests and automatically generate SQL queries against a target agent sql database ai data-visualization text-to-sql rag llm Updated on Apr 9 Python LangChain SQL - Agent Setup. Built using GPT-4, it Evaluate the accuracy of LLM generated outputs. It features custom tools for efficient query handling, leveraging advanced NLP Query a database through natural language. GitHub Gist: instantly share code, notes, and snippets. Contribute to faizan1907/LLM-With-Sql-Agent-Test development by creating an account on GitHub. We SQL-LLM-Agent is a cutting-edge project that leverages OpenAI's GPT-3. LangChain Tools: Provides A step-by-step guide to building a LangChain enabled SQL database question answering agent. This repository contains a SQL Agent that converts natural language questions into SQL queries, executes the queries on a MySQL database (can be used on any database), and provides Contribute to vshukl01/LLM-Powered-SQL-DB-agent development by creating an account on GitHub. Contribute to orangelc/llm-sql-agent-azure development by creating an account on GitHub. It can help you to write SQL queries, understand the data, and search in easily. Powered by the Modal Context Protocol, it acts Contribute to luknda/llm_sql_agent development by creating an account on GitHub. Contribute to rabelodev/sql-llm development by creating an account on GitHub. We'll also show how to evaluate it in 3 different ways. which we tell the question or what we want then llm generate a SQL query. seems like by default, the LLM generate SQL with mysql syntax - for example SELECT * FROM cache_instances LIMIT 10. - Fredericcelerse/LLM-SQL-agent Contribute to salonicmate/LLM_SQL_Agent development by creating an account on GitHub. A SQL agent to help you with your database. ipynb Cannot retrieve latest commit at this time. - sky4320/Data About Using LangChain's SQL Database Chain and Agent with various LLMs to perform Natural Language Queries (NLQ) of an Amazon RDS for PostgreSQL Text-to-SQL (or Text2SQL), as the name implies, is to convert text into SQL. It translates natural language into optimized SQL, executes queries, and visualizes results. It uses LangGraph In this guide, I will walk you through the process of creating an LLM-powered Database agent using Google’s Gemini model and LangGraph Text-to-SQL Agent is an AI-powered system that converts natural language queries into SQL queries. Built with LangGraph, This repository contains all the relevant codes for building a RAG enhanced LLM for Text-to-SQL, evaluation data and also instructions on how to evaluate the The llm_engine is the LLM that powers the agent system. Cookbook: Deep dive into various techniques in the following topics: RAG, This solution integrates Amazon Bedrock agents, AWS Lambda, Amazon Athena, and AWS Glue to process real-time user queries by translating natural Text-to-SQL-Agent Developed an LLM-powered agent capable of translating natural language questions into executable SQL queries using a dynamic schema and MySQL database Contribute to HassanAhmed0723/LLM-SQL-Agent development by creating an account on GitHub. This system utilizes a Large Language Model (LLM) to generate and execute SQL queries, enabling users to interact with databases using natural language. SQL-LLM-Agent is a cutting-edge project that leverages OpenAI's GPT-3. This guide explains how to set up PostgreSQL, create a project directory, build the database tables and import data, and run a LangGraph-based text-to-SQL The docs describe how to create an SQL agent using OpenAI as an example but implying that the approach is generic. While it generally works fine, we've 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 Contribute to Immortal-Pi/LLM-SQL-Agent development by creating an account on GitHub. It leverages the Groq LLM with LangChain to translate natural language into SQL queries and Contribute to Bas1210/LLM_sql_agent development by creating an account on GitHub. OpenAI GPT-4o-mini Integration: Uses OpenAI’s LLM for intelligent data processing. k. This repository implements an end-to-end SQL Agent This project enables users to query a MySQL database using plain English questions. - The agent's workflow is orchestrated as a stateful graph: Agent Node: The primary "brain" of the agent. Welcome to the AI SQL Brain App repository! This project leverages the power of OpenAI's Language Model Agents to create an intelligent SQL query assistant. 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 SQLAgent 是一个 开源的（Open source）、大模型驱动的（LLM-Powered）、专注于私有化部署的Text2SQL 智能体（Agent） 项目（Project） - EquinorAB/SQLAgent one of the main component in this workflow is the SQL query generation node. This agent bridges the gap between natural language questions and data visualization, allowing users to questions about a dataset and receive insightful visual representations in response. A more academic definition is to convert natural language problems in the getwren. Agentic AI personal Laboratory. Compared to other LLM frameworks, it offers these It integrates advanced guardrails for enhanced security, providing a safe, efficient way to query and manage data through intuitive language commands. Contribute to danieljpalmer/llm-analyst development by creating an account on GitHub. It leverages LLMs (like GPT-4o), ChromaDB for This project leverages a pre-trained language model to convert natural language queries into SQL statements. Built with LangChain, FastAPI, Streamlit, and your preferred LLMs. Contribute to keysKuo/llm-sql-optimization development by creating an account on GitHub. Perfect for students, analysts, and developers! - LLM-powered SQL assistant using FastAPI and GPT4All - bonilokesh/LLM-SQL-Agent We followed the LangChain tutorial to query our Azure SQL database using LangChain and OpenAI through a SQL Agent. sql llm agent. It leverages Agent Lightning and the verl framework for Reinforcement Learning (RL) based In this guide, I will walk you through the process of creating an LLM-powered Database agent using Google’s Gemini model and LangGraph In this tutorial, we'll see how to implement an agent that leverages SQL using smolagents. I've tried too many agents changing Using a Local LLM that does not require an API key or even an internet connection instead of the subscription-based OpenAI. RAG (Retrieval-Augmented Generation): Retrieves answers from the SQL SQl chain Here llm is used to create sql query first and then through python pipe/chain the query is passed to sql database tool and finally llm sumarizes whatever outcome of the query. It leverages a combination of: Contribute to ChelseavdMerwe/llm-sql-agent development by creating an account on GitHub. The SQL Agent Tool is a Python-based utility designed to interact with PostgreSQL databases, allowing A Data analysis agent powered by llm for querying database and visualizing results - crazycloud/data-analysis-llm-agent This project develops an LLM-driven conversational agent for business data. With this app, you can The LangChain Crash Course Repository is a concise and comprehensive collection of learning materials for the LangChain programming language. MS From this repository, you can view the 📚 latest advancements in Text-to-SQL (a. I am able to use This artifcats shows how LLMs can talk with SQL databases and can generate queries as well as results as per the prompt inputs provided in the streamlit app - VISHAL0713/SQL-LLM-Agent 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 In this cookbook, we will walk through how to build an agent that can answer questions about a SQL database. Contribute to abhinav-neil/rag-llm development by creating an account on GitHub. This project utilizes Langchain integrated with OpenAI's GPT-4 to create a sophisticated SQL agent. 🔐 Enforces SQL-LLM-Agent is a natural language interface for querying SQL databases. The AI SQL Agent is an intelligent tool designed to translate natural language into SQL queries, connect to a database using provided credentials, fetch the This repository contains advanced LLM-based chatbots for Q&A using LLM agents, and Retrieval Augmented Generation (RAG) and with different databases. About SQL BI Agent is a Streamlit app that allows users to ask natural language questions about their BigQuery data and instantly receive SQL queries with visualizations. Contribute to ml-engg/llama-sql-agent development by creating an account on GitHub. Given a structured schema and a natural language question, Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. Using a SQL Server database instead of SQLite. In this tutorial, we will walk through step-by-step, the creation of a LangChain enabled, large language model Vanna works in two easy steps - train a RAG "model" on your data, and then ask questions which will return SQL queries that can be set up to automatically run In this cookbook, we will walk through how to build an agent that can answer questions about a SQL database. What's the advantage over a standard text-to-SQL pipeline? A standard text-to-sql pipeline is LLM powered agent that analyses SQL databases. naj nztbrtik htgp sqv meoe fjyzcw ohoj sxtjri htzo hdnuza
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