Rag sql agent. SQL Agent: Executes precise SQL queries on structured data.

Rag sql agent. In this video, you'll learn how to use Llama 3 with CrewAI This study explores implementing advanced Retrieval-Augmented Generation (RAG) systems with Graph technology to enhance knowledge-based question-answering and generative AI services. If this is true for you, you should create an AI Agent instead doing lookups into an SQL database. These applications use a technique known as Retrieval Augmented Generation, or RAG. Jan 24, 2025 · RAG translates to “Retrieval Augmented Generation” and is typically implementing by combining a vector database with for instance OpenAI’s embeddings API. This approach aids in locating relevant May 7, 2024 · Today, I’ll introduce you to another amazing dimension of RAG where instead of documents, we will be retrieving data directly from a MySQL database. The sample is build using plain LangChain (app. This section shows you the steps to create a RAG tool. Built-in Agentic Search: Agents can search for information at runtime using 20+ vector databases. Jan 12, 2024 · Dive into the groundbreaking MAC-SQL framework – a multi-agent approach transforming SQL generation from natural language queries in structured data environments. Contribute to TCLee/sql-rag development by creating an account on GitHub. The goal is to get supporting data for the LLM's response. ReAct Agent with Query Engine (RAG) Tools In this section, we show how to setup an agent powered by the ReAct loop for financial analysis. To remain competitive, organizations increasingly recognize that effective data utilization is fundamental to driving innovation. Features: Chat with SQL data. By using vector search to narrow down relevant schema data, we provide context to the LLM, enabling it to generate Feb 9, 2025 · By integrating Agentic RAG with LlamaIndex, developers can build intelligent systems capable of dynamic retrieval, multi-step reasoning, and self-optimizing knowledge generation. RAG Agents consolidating queries over SQL and Document Repositories In this section, we tie everything together by outlining an Agentic AI framework to build RAG pipelines that work seamlessly over both structured and unstructured data stored in Snowflake. Watch: Agentic RAG Overview Understanding Agentic RAG In this video, together we will go through all the steps necessary to design a ChatBot APP to interact with SQL and Tabular Databases using natural language, SQL LLM agents, and GPT 3. MultiModal Agent: Combines the SQL and RAG chains into a unified agent that can route queries to the appropriate system based on the question type. The guide draws from author Tyler Suard real-world experience developing effective RAG solutions for Fortune 500 companies. Simplifying the User Experience: Forget memorizing SQL syntax! Aug 30, 2024 · Check out new AI integrations for your Azure SQL databases. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context —not just keywords. This is a multi-part tutorial: Part 1 (this guide) introduces RAG RAG SQL Agent is a Retrieval-Augmented Generation (RAG) application designed to interact with SQL databases using natural language queries. The GPT-RAG Agentic Orchestrator provides a range of agent strategies to handle different types of queries and data interactions. Agentic RAG is an agent based approach to perform question answering over Architecture Overview These scenarios extend the GPT-RAG Agentic Orchestrator's ability to convert user requests into SQL or DAX queries, supporting Azure SQL Database and Microsoft Fabric as data sources. Dec 21, 2023 · To facilitate your agent’s understanding of how to use these functions, I propose employing a technique known as Retrieval Augmented Generation (RAG). In this tutorial we about the book Enterprise RAG teaches you to build production-ready RAG systems. sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. This innovative solution promises to revolutionize enterprise data management, providing a more intuitive, flexible, and comprehensive data Jan 6, 2024 · Delve into the world of Retrieval Augmented Generation (RAG) as it revitalizes JavaScript SQL interactions, offering insights on incorporating AI-driven dynamics into database queries. Nov 17, 2024 · 4. Jul 14, 2024 · Here we are about to create a build a team of agents that will answer complex questions using data from a SQL database. You will build a database agent where the user can interact with the application in their language instead of using SQL queries. Don't miss this opportunity to see how you can build a full-stack end-to-end solution in May 6, 2025 · This process is known as Retrieval Augmented Generation (RAG) and Azure SQL Database and Fabric SQL database have many features that support this new pattern, making it a great database to build intelligent applications. Oct 18, 2024 · Learn how to implement RAG-to-SQL on Google Cloud, streamlining SQL query generation for powerful data insights. Mar 31, 2025 · Tool Invocation: The agent selects and uses a tool (e. Run the line below to install required dependencies: 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 performance by test-suite-sql-eval through Docker and customize your Text-to-SQL evaluation pipeline based on own data by Langsmith. 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. The system actively owns its reasoning process, rewriting failed queries, choosing different retrieval methods, and integrating multiple tools—such as vector search in Azure AI Search, SQL databases, or custom APIs—before finalizing its answer. Nov 14, 2024 · Learn to build with Ultimate Chatbot using RAG, NL2SQL and Semantic Kernel to query all your data, structured and unstructured. This guide covers practical steps, best practices, and optimization techniques to ensure seamless connectivity between retrieval-augmented generation systems and structured databases. This solution is based on the reference architecture Infrastructure for a RAG-capable generative AI application using Vertex AI and AlloyDB for PostgreSQL, but it's designed to help you get started and learn how to use RAG at a lower cost. Feb 17, 2025 · Great improve text-to-SQL generation using super simple RAG solution for adding critical prompt context. Note that you can plug in any LLM to use as a ReAct agent. This project combines RAG technology and large language models to generate accurate SQL queries by retrieving relevant domain knowledge and incorporating the user&#39;s natural language queries. Assessment & Refinement: The agent evaluates the retrieved data and refines its query or chooses a different tool if needed. com/course/advanced LANGCHAIN ON AZURE: https://www. To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. For both options, creating a tool has the same steps. But RAG doesn't always work for our use cases. Mar 14, 2024 · Learn how to master RAG SQL integration for enhanced data retrieval and analysis. See the AI in action and understand why is going to change the way we all work and build applications, for real! Chapters 00:00 - Introduction 01:30 - Demo 10:39 - Getting started Recommended resources Repository Related episodes Data Exposed | Microsoft Learn To Jun 12, 2024 · We are releasing Function RAG API under a different domain and branding to distinguish it from the core open-source Python package. 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. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. 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). Learn to utilize agent-based retrieval, triage logic, query rewriting, and other cutting-edge strategies for effective RAG. Jan 9, 2025 · Agentic AI Data PlatformBuild RAG Apps 90% Faster Keep your AI up-to-date in real-time with Vectorize RAG pipelines Try It Free Azure SQL DB - Retrieval Augmented Generation (RAG) with OpenAI In this repo you will find a step-by-step guide on how to use Azure SQL Database to do Retrieval Augmented Generation (RAG) using the data you have in Azure SQL and integrating with OpenAI, directly from the Azure SQL database itself. [SalesOrderHeader], [Sales]. With advanced vector-based semantic search, discover precise insights tailored to your data, while Copilot in Azure streamlines troubleshooting and T-SQL query authoring. Apr 2, 2024 · 本文探讨了如何通过合并高级分析功能来增强SQL代理的功能，特别是利用Teradata的高级分析功能和检索增强生成（RAG）技术。通过集成RAG，SQL代理可以更准确地生成SQL语法，提高查询效率和准确性。 The SQL Agent uses a SQL database as a data source. Let’s see an option that can be implemented right away. Explore step-by-step instructions and best practices. These agents leverage agentic design patterns reflection, planning, tool use, and multiagent collaboration to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt Self-correcting Text-to-SQL Master your knowledge base with agentic RAG Orchestrate a multi-agent system Build a web browser agent using vision models Using different models Human-in-the-Loop: Customize agent plan interactively Async Applications with Agents As you build your own Agentic RAG systems, consider experimenting with different retrieval methods, agent architectures, and knowledge sources to find the optimal configuration for your specific use case. Jun 14, 2024 · 文章浏览阅读3. Mar 9, 2011 · About AI Agent RAG & SQL Chatbot enables natural language interaction with SQL databases, CSV files, and unstructured data (PDFs, text, vector DBs) using LLMs, LangChain, LangGraph, and LangSmith for retrieval and response generation, accessible via a Gradio UI, with LangSmith monitoring. In this Blog we will build a multi AI agent with RAG using Langraph and AstraDB with integration with the Llama 3. Uses dynamic few-shot examples and rules to improve query construction. Refer to AI Agent for more information on the AI Agent node itself. I think it will yield me better results if I can layer in project documentation and examples. Discover the potential of Agentic RAG to streamline complex information retrieval tasks, enhancing accuracy and facilitating decision-making across diverse domains. Full details and video recording available here: RAG on Azure SQL Server. In this blogpost we will cover how to expand the standard SQL Toolkit with some very useful example extra tools. ai/oss agent bigquery charts sql postgresql bedrock business-intelligence openai spreadsheets vertex genbi text-to-sql rag text2sql duckdb llm anthropic sqlai text-to-chart Readme AGPL-3. These are applications that can answer questions about specific source information. Discover how Retrieval SQLDatabase Toolkit This will help you get started with the SQL Database toolkit. You can create an agent's tool during or after creating the agent. Traditional RAG implementations rely on pre-defined Dec 13, 2023 · In this lesson, our focus is on revealing how the RAG pipeline of LlamaIndex transforms a standard database into an interactive system, driven by agent-based technology for queries and responses SQL Agent Copy page This example shows how to build a text-to-SQL system that: Uses Agentic RAG to search for table metadata, sample queries and rules for writing better SQL queries. Mar 9, 2011 · AgentGraph: Intelligent SQL-agent Q&A and RAG System for Chatting with Multiple Databases This project demonstrates how to build an agentic system using Large Language Models (LLMs) that can interact with multiple databases and utilize various tools. We'll walk you through the entire process, Apr 15, 2025 · Learn how generative AI and retrieval augmented generation (RAG) patterns are used in Azure AI Search solutions. Here’s how a real interaction might play out: Human: Hi, I want to know who is the most profitable customer. This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application. udemy. The distinguishing quality of an agentic system is its ability to own its reasoning process. Chat with preprocessed CSV and XLSX data. It highlights the use of SQL agents to efficiently query large databases. Tools within the SQLDatabaseToolkit are designed to interact with a SQL database. 但是，我们可以通过创建一个 RAG agent 来缓解这些问题：非常简单，一个配备了检索器工具的 agent！ 这个 agent 将会： 自己制定查询，以及 在需要时进行批判性评估以重新检索。 因此，它应该自然而然地恢复一些高级 RAG 技术！ Mar 7, 2025 · By the end of this guide, you’ll have a chatbot capable of dynamically generating SQL queries from user inputs, executing those queries, and providing insightful responses based on retrieved data implement in Langflow. [Customer], [Sales]. Step-by-step tutorial for developers to create task-oriented agents. The fundamental concept behind agents involves employing Aug 2, 2024 · I am following the SQLAgent tutorial from Langgraph and adding RAG to it. It is very likely that a lot of your data is already stored or will be stored in Azure SQL, […] Aug 21, 2023 · A step-by-step guide to building a LangChain enabled SQL database question answering agent. For detailed documentation of all SQLDatabaseToolkit features and configurations head to the API reference. Jun 2, 2025 · Agent 2 is the query expert. How to Implement Agentic RAG Using LangChain: Part 1 Learn about enhancing LLMs with real-time information retrieval and intelligent agents. Feb 27, 2025 · Learn how to build an Agentic RAG pipeline from scratch, integrating local data sources and web scraping to generate context-aware responses to user queries. Go to the notebook. Dec 13, 2024 · Let RAG handle broad questions for a natural, conversational feel. This project leverages Retrieval Augmented Generation (RAG) to enhance SQL query generation for Oracle Autonomous Data Warehouse (ADW) using the OCI Generative AI service and Marqo AI. Understanding the SQL Agent Workflow In this 2nd of 3 part video, we will use Langchain to query a SQL database by passing the schema to the prompt with the question using the SQLDatabase object In Generative AI Agents, each agent must have one or more tools. With Retrieval Augmented Generation, you can bridge structured data with generative AI, enhancing natural language queries across applications. RAG with LLM agents for SQL & graph databases. Repeat Until Satisfied: This loop continues until the agent deems its response satisfactory. Text-to-SQL Guide (Query Engine + Retriever) This is a basic guide to LlamaIndex's Text-to-SQL capabilities. Jan 14, 2025 · What is an Agentic RAG? An Agentic RAG builds on the basic RAG concept by introducing an agent that makes decisions during the workflow: Basic RAG: Retrieves relevant information from a database A chat which uses a SQL Agent with BigQuery to introspect and query a dataset What I am struggling to figure out now, is how do I combine them? I want to have the vector store augment the prompts used by the SQL agent. In NL2SQL strategies, the orchestrator uses ODBC drivers to connect to Azure SQL databases. Key Features of Function RAG Template-Based SQL Generation : By converting training pairs into templates, Function RAG ensures that the SQL generated is both accurate and relevant to the user's query. Don't miss this opportunity to see how you can build a full-stack end-to-end solution in just 15 minutes! But we can alleviate these problems by making a RAG agent: very simply, an agent armed with a retriever tool! This agent will: Formulate the query itself and Critique to re-retrieve if needed. 5. My first approach was to convert each entry into a JSON string, treat it as a document for indexing and build a simple RAG on top. Traditional Text2SQL requires model fine-tuning, which can significantly increase deployment and maintenance costs when used in enterprise settings alongside RAG or Agent components. This agent is valuable for building natural language interfaces to databases. The result is an automated chatbot Dec 6, 2023 · Therefore, RAG with semantic search is not tailored for answering questions that involve analytical reasoning across all documents. By the end of the lesson, you will deploy your Azure database SQL OpenAI service instance and test API. Be sure that the tables actually exist by calling sql_db_list_tables first! Imagine giving your AI the power to work directly with your private data. The agent has access to two "tools": one to query the 2021 Lyft 10-K and the other to query the 2021 Uber 10-K. - Nov 18, 2024 · You will learn grounding techniques, RAG, to start building AI agents. This guide explores how to build Agentic RAG with LlamaIndex, its key components, implementation steps, and real-world applications. It leverages advanced language models to generate SQL queries, retrieve relevant data, and provide human-readable answers. py) or using LangGraph (app-langgraph. Built with LangGraph, LangChain, and modern LLM frameworks. getwren. Jan 23, 2024 · To achieve this I’ve began using PGVector within SQL, PostgresSQL is a tried and true database and if you’re like me and don’t want manage several database providers and wanted to get . A SQL-based RAG agent with guardrails using Mixtral-8x7b (LangChain) - cvarrei/SQLAgent_llm Q&A-and-RAG-with-SQL-and-TabularData: Q&A-and-RAG-with-SQL-and-TabularData is a chatbot project that utilizes GPT 3. That's what happens when you connect agents to a SQL database. The idea is to improve the retrieval part so that it will not be limited to vector search only. May 5, 2025 · Learn about retrieval augmented generation (RAG) on Databricks to achieve greater large language model (LLM) accuracy with your own data. It can recover from errors by running a generated query, catching the traceback and regenerating it Jul 2, 2024 · 在传统的意义上，RAG 主要是从文档中检索用户想要的数据，从而提高大模型的能力，减少幻觉问题。今天，我们从另一个维度介绍RAG，RAG不从文档中获取数据，而是从MySQL数据库检索数据。我们可以使用LangChain SQL Agent结合聊天历史信息构建一个多层RAG聊天机器人。 一、架构 整体架构，如上图所示 Build a Retrieval Augmented Generation (RAG) App: Part 1 One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. AI Agent Router: Analyzes and routes user queries to the most suitable processing agent. We present HM-RAG, a novel Hierarchical Multi-agent Multimodal RAG framework that pioneers collaborative intelligence for dynamic May 29, 2024 · This will discuss what query pipelines are, why they are important and provide a practical example by building a Text to SQL RAG with query pipelines. , vector database query, SQL call) to gather information. We'll start with the basics of Semantic Kernel, move on to implementing RAG patterns using Azure SQL DB's vector search capabilities, and then have a look at building AI Agents. The Feb 14, 2024 · The RAG agent is used for scenarios where the developer does not have access to a substantial set of sample Question<>SQL pairs (golden SQL) for fine-tuning or training the LLM. Discover techniques and best practices for streamlining data retrieval, optimizing query generation, and ensuring scalable maintenance, while also envisioning the future of intelligent, self-adapting SQL Nov 29, 2024 · Learn to build a custom AI agent using LangGraph with RAG, NL2SQL, and Web Search. Data powers real-time insights and supports advanced agentic AI systems Mar 22, 2025 · Data meets GenAI: Building a smart Data Analysis Agent that understands both your data and visualization needs In the rapidly evolving AI landscape, Retrieval Augmented Generation (RAG) has become… Jan 30, 2024 · Agentic RAG, where an agent approach is followed for a RAG implementation adds resilience and intelligence to the RAG implementation. Apr 13, 2025 · While Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge, conventional single-agent RAG remains fundamentally limited in resolving complex queries demanding coordinated reasoning across heterogeneous data ecosystems. 1 open source model using Groq API. Understanding Agentic RAG Retrieval-Augmented Generation (RAG) is an AI technique RAG: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - View Notebook Function Inception: Enable AutoGen agents to update/remove functions during conversations. g. SQL Agent: Executes precise SQL queries on structured data. Rag Sql Agent is a question answering tool designed to assist users in analyzing travel-related data. Contribute to abhinav-neil/rag-llm development by creating an account on GitHub. We then show how to buid a TableIndex over the schema to dynamically retrieve relevant tables during query-time. What is SQL RAG? SQL RAG stands for SQL-based Retrieval-Augmented Generation. This video guide shows you how to create a custom agent that can query either your LlamaCloud index for RAG-based retrieval or a separate SQL query engine as a tool. Accurate Text-to-SQL Generation via LLMs using RAG 🔄. Multi-Agent Chatbot with RAG and SQL Database Integration A sophisticated conversational AI system that combines Retrieval-Augmented Generation (RAG) with SQL database agents to provide intelligent responses across multiple data sources. Selecting the appropriate strategy ensures that the orchestrator operates efficiently and meets the specific needs of your application. Implementation Steps: Create Nov 6, 2024 · This guide helps you understand and deploy the Generative AI RAG with Cloud SQL solution. It enables users to ask questions in natural language and generates SQL queries to retrieve the required information. 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. We will Sep 24, 2024 · RAGFlow introduces the Text2SQL feature in response to community demand. Aug 29, 2024 · Q->SQL知识库：在Text2SQL生成过程中，对大语言模型提供samples往往能提高生成的SQL语句的质量，因此在Q->SQL知识库中需要提供自然语言->对应的SQL语句的samples，如果能够提供所查询的数据库Q->SQL的samples，则Text2SQL结果质量更高。 Nov 4, 2024 · MY COURSES: ADVANCED RAG WITH LANGCHAIN: https://www. In this guide we'll go over the basic ways to create a Q&A system over tabular data Apr 28, 2024 · Output for Azure SQL Studio Conclusion By integrating RAG with SQL databases using the combined capabilities of Azure, OpenAI, and LangChain, this approach not only simplifies the data querying process but also enhances the quality of the insights derived. It is a good illustration of multi-agent orchestration. The reference architecture of such a RAG Agent is illustrated in Fig. py) to define the RAG process. It writes SQL queries using only the relevant tables and a clear goal, as instructed by Agent 1. - vanna-ai/vanna Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. Feb 18, 2025 · Agentic RAG simplifies text-to-SQL by modularizing tasks into tools like query transformation, hybrid search, and re-ranking, ensuring accuracy and scalability. So it should naively recover some advanced RAG techniques! Let’s build this system. Apr 28, 2024 · Output for Azure SQL Studio Conclusion By integrating RAG with SQL databases using the combined capabilities of Azure, OpenAI, and LangChain, this approach not only simplifies the data querying Jun 23, 2023 · RAG：针对表结构、Gold SQL、指标计算公式等数据对象的高性能RAG的算法，百万数据检索小于1s，召回率大于95%。 多卡推理：基于vLL的多卡推理的部署方案。 Feb 19, 2025 · Agentic RAG System Architectures: Explore dynamic frameworks merging RAG and AI Agents to enhance decision-making, retrieval, and more. 2k次，点赞18次，收藏24次。在第二层，SQL Agent首先获取到用户的问题，然后要求 LLM 根据用户的问题创建 SQL 查询，使用内置函数在MySQL数据库上运行查询。在这里，我们使用的是 ChatPromptTemplate，如果你真的研究它，你会看到它是如何专门编写的，用于创建和运行 SQL 查询。在下一段 We want to build a RAG system based on a single SQL table that contains multiple long text columns. Dec 9, 2024 · This agent will be capable of understanding questions about a SQL database, generating appropriate queries, and providing human-readable answers. It can understand natural language questions, convert them into SQL queries, execute the queries, and present the results in a user-friendly format. Jan 15, 2025 · Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields. Agno provides state-of-the-art Agentic RAG, fully async and highly performant. Apr 9, 2025 · When paired with databases, RAG enables LLMs to generate SQL queries by retrieving the appropriate schema and understanding the context of user questions. Allow your AI Agent to convert pinpoint questions into SQL queries, pulling exact data in real-time. com/course/langchaimore 5 days ago · In the current digital environment, applications are expected to offer more than basic functionality, they must demonstrate intelligence, adaptability, and provide seamless user experiences. RAGFlow’s RAG-based Text2SQL leverages the existing (connected) large language model (LLM), enabling seamless integration with other RAG 在传统的意义上，RAG 主要是从文档中检索用户想要的数据，从而提高大模型的能力，减少幻觉问题。今天，我们从另一个维度介绍RAG，RAG不从文档中获取数据，而是从MySQL数据库检索数据。我们可以使用LangChain SQL A… This video teaches you how to build a SQL Agent using Langchain and the latest Llama 3 large language model (LLM). Sep 19, 2024 · For instance, when answering a business intelligence query, an agent might first use RAG to retrieve relevant documents, then execute a query on a SQL database to verify the data, and finally call May 19, 2024 · RAG模型训练完成后，可以用自然语言直接提问。 Vanna会利用RAG与LLM生成SQL，并自动运行后返回结果。 03 COLD WEATHER vanna的扩展与定制化 从上述的vanna原理介绍可以知道，其相关的三个主要基础设施为： Database ，即需要进行查询的关系型数据库 RAG Chain: Extracted text is processed into a vector store for semantic search and query answering. Architecture: How LLMs Chat With Databases In this tutorial, we’ll build an LLM-powered agentic graph using LangChain and LangGraph to combine RAG (Retrieval-Augmented Generation) with SQL agents. Get started now! Oct 12, 2023 · Leverage the power of Retrieval Augmented Generation (RAG) to connect your database with your Large Language Models and make it context aware. Output Guardrail Agent: Validates responses to ensure they remain on-topic and accurate. What is RAG Search and how to use it? RAG search allows the agent to check what are the things the agent already know about a specific topic (requires some data to be embedded in workspace) You can use RAG search by asking the agent something like @agent can you check what you already know about AnythingLLM? Apr 26, 2025 · SQL agents with LangGraph 🦜🕸️ Creating accurate SQL queries with LLMs becomes challenging as query complexity increases. We first show how to perform text-to-SQL over a toy dataset: this will do "retrieval" (sql query over db) and "synthesis". 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 Oct 30, 2024 · Boosting Accuracy: RAG adds context to the Text-to-SQL model, guiding it to generate SQL queries that are sharp, precise, and on target. Apr 28, 2024 · Output for Azure SQL Studio Conclusion By integrating RAG with SQL databases using the combined capabilities of Azure, OpenAI, and LangChain, this approach not only simplifies the data querying Advanced Multi-Agent Architecture: Agno provides an industry leading multi-agent architecture (Agent Teams) with reasoning, memory, and shared context. Sep 5, 2023 · In this way, we get the best of both worlds: anyone can run the standard SQL Agent with minimal setup while at the same time being able to incorporate extra tools that add relevant information to the prompt at inference time. Azure SQL DB, Langchain, LangGraph and Chainlit Sample RAG pattern using Azure SQL DB, Langchain and Chainlit as demonstrated in the #RAGHack conference. Jan 21, 2025 · Learn how to build powerful RAG chatbots with n8n's visual workflow automation. 🛠️. Jan 5, 2025 · Learn to build a scalable, modular multi-agent system using LangGraph with step-by-step guidance on agent orchestration and integration We’ve shown you how to make a very basic RAG (retrieval-augmented generation) system for natural language question-answering that uses an SQL database as an information source. Dec 12, 2024 · RAG is amazing, and it's arguably 80% of our revenue. Jan 23, 2025 · We’re building a supercharged Langflow agent powered by multiple tools working together: RAG — Your knowledge supercharger RAG helps your bot think smarter by pulling in data before responding. Adding into this, we are also solving another Jan 23, 2025 · RAG needs to be improved in order make that possible. Mar 17, 2025 · Integrating RAG with SQL databases enhances data retrieval and processing. Jul 23, 2025 · Agentic RAG is an advanced version of Retrieval-Augmented Generation (RAG) where an AI agent retrieves external information and autonomously decides how to use that data. 0 license Code of conduct What is RAG Search and how to use it? RAG search allows the agent to check what are the things the agent already know about a specific topic (requires some data to be embedded in workspace) You can use RAG search by asking the agent something like @agent can you check what you already know about AnythingLLM? Dec 14, 2024 · 在传统的意义上，RAG 主要是从文档中检索用户想要的数据，从而提高大模型的能力，减少幻觉问题。今天，我们从另一个维度介绍RAG，RAG不从文档中获取数据，而是从MySQL数据库检索数据。我们可以使用LangChain SQL Agent结合聊天历史信息构建一个多层RAG聊天机器人。 Jun 11, 2025 · What is retrieval-augmented generation? In the simplest form, a RAG agent does the following: Retrieval: The user's request is used to query an outside knowledge base such as a vector store, keyword search, or SQL database. Setting up a SQLite database Creating a function to execute SQL queries Building an agent for querying SQL databases Running the agent with various types of queries By implementing these techniques, we'll expand our agentic RAG system to handle structured data in SQL databases, complementing our previous work with tabular data in pandas. Optimize workflows Apr 9, 2024 · Combining retrieval-augmented generation (RAG) with SQL makes it easier to apply LLMs to wring more insights from your company data. Simple prompts suffice for basic SQL, but complex joins and logic require detailed prompts, iterative feedback, and error handling. Within the context of a team, an agent can be envisioned as an individual Mar 31, 2024 · In Native RAG the user is fed into the RAG pipeline which does retrieval, reranking, synthesis and generates a response. Dec 10, 2024 · The SQL agent can be also integrated in a more general architecture, with Retrieval Augmented Generation (RAG), enabling users to combine analysis on structured and unstructured data. 5, Langchain, SQLite, and ChromaDB and allows users to interact (perform Q&A and RAG) with SQL databases, CSV, and XLSX files using natural language. The architecture enables efficient data retrieval, processing, and autonomous task execution while maintaining security through its Assistant API layer. Hi Community, Traditional keyword-based search struggles with nuanced, domain-specific queries. Build a Question Answering system over SQL data. Nov 6, 2024 · At its core lies a Master Agent that orchestrates specialized agents, each enhanced with RAG capabilities for contextual decision-making. Mar 24, 2025 · Explore how advanced RAG systems with NL-to-SQL agents enhance data retrieval, combining human oversight and few-shot learning for precise SQL queries. May 4, 2024 · Here we will build reliable RAG agents using LangGraph, Groq-Llama-3 and Chroma, We will combine the below concepts to build the RAG Agent. Sep 7, 2024 · This multi-agent system is designed to manage financial and consumption analysis tasks efficiently: · Financial Analysis: Uses the RAG system to retrieve and process unstructured data such as 🤖 Chat with your SQL database 📊. This is often achieved via tool-calling. RAG Agent: Leverages semantic, vector-based search for in-depth insights. Jul 9, 2025 · Agent Architecture: A Langchain based ReAct (Reasoning and Acting) agent architecture was employed to process queries, select the appropriate database tool, and generate SQL queries when necessary. Aug 26, 2024 · Retrieval Augmented Generation, or RAG, is one of the hottest topics at the moment as it opens up the possibility of interacting with data using natural language, which is a long-time dream finally coming true. This step-by-step guide demonstrates how to connect to any knowledge source, index it in a vector database, and create an AI-powered chatbot that provides accurate, context-aware answers. Mar 28, 2025 · Build Your Own Agent This example demonstrates how to deploy an SQL use case, but agents are dynamic, and you may want to register your own agent within the architecture. Join us for an exciting demonstration on how to transform raw data in a database into a searchable format using Natural Language Processing (NLP). Provides an interactive Streamlit UI for users to query the database. 5. You will learn how to leverage Retrieval-Augmented Generation (RAG), vectors, NL2SQL, and agents, all within T-SQL, to create a powerful and scalable solution. A common application is to enable agents to answer questions using data in a relational database, potentially in an Feb 5, 2025 · Tutorial: Trace an Agentic RAG App This companion notebook will help you build and trace an agentic RAG system using LlamaIndex’s ReAct agent framework combined with vector and SQL query tools, and Arize Phoenix. In traditional RAG, system retrieves information and generates output in one continuous process but Agentic RAG introduces autonomous decision-making. Agent1: I need to query the database using [Sales]. ojne famnx kygk xqo aqrcjd wlj jzk entas btpqw uezv