Llamaindex sql agent. Oct 30, 2024 · Tackling Complex Questions: With RAG, Text-to-SQL, and LlamaIndex, we can create answers that draw on both structured (databases) and unstructured (text) data, bringing depth and context to answers. workflow import FunctionAgent, ReActAgent from llama_index. Here the airlines reviews dataset from Kaggle is used as input data. Note that you can plug in any LLM to use as a ReAct agent. Agents offer the ability to do complex, sequential reasoning on top of any query DAG that you have setup. We explore how FunctionAgent can compare/replace existing workflows solved by our retrievers/query engines. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result. Adding Logging capablities import logging import sys logging Jul 12, 2023 · Today we’re incredibly excited to announce the launch of a big new capability within LlamaIndex: Data Agents. Auto retrieval Joint SQL and vector search Aug 12, 2023 · LlamaIndex, through its ingenious integration of SQL and RAG, offers businesses a powerful tool to glean actionable insights from such datasets. LangChain: The best framework for building agents. 5-turbo-instruct Note that you can plug in any LLM that exposes a text completion endpoint. On one hand, many components within LlamaIndex are "agentic" - these make automated decisions to help a particular use case over your data. SQL Agent: A subset of the above is a "text-to-SQL assistant" that can interact with a structured database. workflow import Context agent = FunctionAgent (tools=query_engine_tools, llm=OpenAI (model="gpt-4o")) # context to hold the session/state ctx = Context (agent) LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Together, Databricks and LlamaIndex help companies build robust agent workflows and unlock insights from large volumes of unstructured data. 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 Aug 17, 2023 · LlamaIndex for text-to-SQL inference against any SQL database. If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙. Text-to-SQL Guide (Query Engine + Retriever) This is a basic guide to LlamaIndex's Text-to-SQL capabilities. Agents Putting together an agent in LlamaIndex can be done by defining a set of tools and providing them to our ReActAgent implementation. Jul 12, 2023 · LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data. An agent that takes a natural language query from a user, generates an SQL query, and iteratively debugs the SQL query until the original question is answered - m13v/sql_agent_llamaindex Oct 24, 2024 · In this article, we’ll create a Text-to-SQL analysis agent using LlamaIndex workflows. In general, FunctionAgent should be preferred for LLMs that have built-in function calling/tools in their API, like Openai, Anthropic, Gemini, etc. NOTE: deprecated in favor of SQLTableRetriever, kept for backward compatibility. Additionally, we currently only support SQL databases (including remote databases which you can configure). Technology Stack Llama-Index : LlamaIndex is SQL Query Engine with LlamaIndex + DuckDB # This guide showcases the core LlamaIndex SQL capabilities with DuckDB. Here are some core resource guides to refer to. Step 4: Implement Multi-Hop Retrieval with LlamaIndex Instead of a single-pass retrieval, Agentic RAG iterates over multiple retrieval cycles: This retrieves information in multiple steps, improving search accuracy and eliminating irrelevant or misleading data. This page highlights key examples to help you get started. We're using it here with OpenAI, but it can be used with any sufficiently capable LLM: Feb 9, 2025 · This AI agent dynamically refines search queries, ensuring more precise retrieval. ). It provides the following tools: Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc. In the navigation to the left, you will also find many example notebooks, displaying the usage of various llama-index components and use-cases. The Agent can be used for retrieving data from a database (sqlite) using SQL queries. Learn the step-by-step workflow of developing Natural Language to SQL applications using LlamaIndex modules. By seamlessly blending structured SQL queries with the abstraction of natural language processing, we’ve showcased a streamlined approach to transform vague user queries into precise, informative Examples LlamaIndex provides a rich collection of examples demonstrating diverse use cases, integrations, and features. Contrast this with the term "agentic", which generally refers to a superclass of agents, which is any system with LLM decision making in the process. Learn how to build an AI-powered SQL chat app using LlamaIndex, Groq, and Hugging Face. They are capable of the following: Perform automated search and retrieval over different types of data - unstructured, semi-structured, and structured. May 28, 2023 · Summary In this article, we showcase a powerful new query engine ( SQLAutoVectorQueryEngine ) in LlamaIndex that can leverage both a SQL database as well as a vector store to fulfill complex natural language queries over a combination of structured and unstructured data. The input to the PandasQueryEngine is a Pandas dataframe, and the output is a response. This page walks through the three most common patterns, when to choose each one, and provides a minimal code sketch for every approach. Oct 24, 2024 · In this article, we’ll create a Text-to-SQL analysis agent using LlamaIndex workflows. A data agent is initialized with set of APIs, or Tools, to interact with; these APIs can be called by the agent to return information or modify state. Special mention to the awesome Llama 2 tutorial from Anyscale that helped to inspire this project. Agents Build powerful AI assistants with LlamaIndex's agent capabilities Dec 6, 2023 · We are thrilled to showcase the integration of Waii with LlamaIndex to create an agent capable of executing various text-to-SQL tasks and validating the data based on a PDF. The implementation utilizes Llama-index as the framework and is demonstrated using the Python programming language. This agent will understand user questions in natural language, turn them into SQL commands, and run them This agent takes a natural language query from a user, generates an SQL query, and iteratively debugs it until the original question is answered. Query your database with natural language—no SQL required! 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. Given an input task, the data agent uses a reasoning loop to decide which tools to use, in which sequence, and the parameters to call each tool. We first show how to perform text-to-SQL over a toy dataset: this will do “retrieval” (sql query over db) and “synthesis”. I'm a RAG beginner and want to learn the basics: Take a look at our "Learn" series of guides. SQL Router Query Engine # In this tutorial, we define a custom router query engine that can route to either a SQL database or a vector database. And it is a core tool that you use while building an agent-based app. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs. CrewAI: Easy development if you're good at defining goals and writing backstories for each agent. Feb 11, 2024 · After briefly introducing RAG and LlamaIndex, let’s refocus on our main discussion topic, which concerns the process of connecting to and querying an SQL database using LlamaIndex. The notebook guides you through the setup, configuration, and execution of the AI agent, leveraging these powerful tools for advanced natural language processing tasks. However, if goals aren't clear, agents can perform unnecessary actions. We first show how to perform text-to-SQL over a toy dataset: this will do "retrieval" (sql query over db) and "synthesis". """ def __init__( self, sql_database: SQLDatabase, table_retriever . This project highlights LlamaIndex Text-to-SQL Guide (Query Engine + Retriever) # This is a basic guide to LlamaIndex’s Text-to-SQL capabilities. This query engine can leverage the expressivity of SQL over structured data, and join it with unstructured context from a Agents In LlamaIndex, we define an "agent" as a specific system that uses an LLM, memory, and tools, to handle inputs from outside users. Jul 16, 2024 · LlamaIndex: Elevate AI capabilities with dynamic data processing and intelligent decision-making. LlamaIndex is a "data framework" to help you build LLM apps. """ def __init__( self, sql_database: SQLDatabase, table_retriever Example of multi-document queries Building a multi-document agent over the LlamaIndex docs - Text to SQL Resources LlamaIndex has a lot of resources around QA / RAG. This gives you flexibility to enhance text-to-SQL with additional techniques. core. To create an agent in LlamaIndex, it takes only a few lines of code: Building an Agent around a Query Pipeline This agent takes a natural language query from a user, generates an SQL query, and iteratively debugs it until the original question is answered. Apr 18, 2025 · Use Databricks Unity Catalog to integrate SQL and Python functions as tools in LlamaIndex workflows, enabling indexing and querying of large datasets for LLMs. An "agent" is an automated reasoning and decision engine. class SQLTableRetrieverQueryEngine(BaseSQLTableQueryEngine): """SQL Table retriever query engine. Integrate MyScaleDB for advanced vector search and scalable data management within an efficient AI agent framework. By seamlessly blending structured SQL queries with the abstraction of natural language processing, we’ve showcased a streamlined approach to transform vague user queries into precise, informative OpenAI Agent + Query Engine Experimental Cookbook # In this notebook, we try out the OpenAIAgent across a variety of query engine tools and datasets. This demo incorporates a Query Planning Tool and an OpenAI agent, this allows the agent to perform advanced planning based on a user query, all via a single agent. It is given a set of tools, which can be anything from arbitrary functions up to full LlamaIndex query engines, and it selects the best available tool to complete each May 13, 2025 · For example, the current implementation only allows you to make use of memory as part of the backend of an agent. Dec 31, 2023 · This article outlines the process of employing a LLM in conjunction with a SQL database by establishing a connection between OpenAI’s GPT-3. These connectors are compatible with APIs, PDFs, SQL, and more, allowing seamless integration of data for natural language access and retrieval. We show you how Oct 13, 2023 · How to connect DB’s using Llamaindex ask queries in NLP Step install LLama index& MY SQL !pip install llama-index pymysql -q 2. LlamaIndex provides a lot of advanced features, powered by LLM's, to both create structured data from unstructured data, as well as analyze this structured data through augmented text-to-SQL capabilities. It is recommended to take Aug 12, 2023 · LlamaIndex, through its ingenious integration of SQL and RAG, offers businesses a powerful tool to glean actionable insights from such datasets. May 1, 2025 · Databricks Ventures has invested in LlamaIndex to strengthen our relationship and accelerate the development of knowledge AI agents. Building a Custom Agent # In this cookbook we show you how to build a custom agent using LlamaIndex. SQL Query Engine with LlamaIndex + DuckDB This guide showcases the core LlamaIndex SQL capabilities with DuckDB. May 29, 2024 · This article discusses about the Query Pipeline feature in LlamaIndex. This project highlights LlamaIndex Workflows for Advanced Text-to-SQL In this guide we show you how to setup a text-to-SQL workflow over your data with our workflows syntax. Check out this guide to see how to build an agent from scratch. 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. We’d love to provide the option of using memory as one of the suite of tools for a tool calling agent. The LLM infers dataframe operations to perform in order to retrieve the result. Aug 17, 2023 · LlamaIndex for text-to-SQL inference against any SQL database. The easiest way to build a custom agent is to simply subclass CustomSimpleAgentWorker and implement a few required functions. indices. This lets you add arbitrarily complex reasoning logic on top of your RAG pipeline. It is recommended to take precautions as needed May 29, 2024 · A: LlamaIndex has a inbuilt Text-to-SQL functions and methods like SQL table retrieval query engine to retrieve data from database and generate response for the users. We try two different LLMs: gpt-3. We need to setup SQL Alchemy connection and tested it out against BigQuery dataset created in erlier step. 5-turbo gpt-3. Query your database with natural language—no SQL required! In this notebook, we try out the FunctionAgent across a variety of query engine tools and datasets. We show these in the below sections: Query-Time Table Retrieval: Dynamically retrieve relevant tables in the text-to-SQL prompt. Jan 30, 2024 · Answer: LlamaIndex connectors are used to import existing data from various sources and formats into the LlamaIndex ecosystem. agent. Considering the header image, the Query Plan Tool rely on other tools for input. struct_store. We explore how OpenAIAgent can compare/replace existing workflows solved by our retrievers/query engines. That's where LlamaIndex comes in. Provides an advanced retrieval/query interface over your Example of multi-document queries Building a multi-document agent over the LlamaIndex docs - Text to SQL Resources LlamaIndex has a lot of resources around QA / RAG. Following this investment, Databricks will deepen its technical integration with LlamaIndex and make it easier for Structured Data # A Guide to LlamaIndex + Structured Data # A lot of modern data systems depend on structured data, such as a Postgres DB or a Snowflake data warehouse. This guide showcases the core LlamaIndex SQL capabilities with DuckDB. This agent will understand user questions in natural language, turn them into SQL commands, and run them I developed a sophisticated AI agent using LlamaIndex, enabling SQL queries, arithmetic operations, vector search, and summarization with historical chat context. Arbitrary code execution is Jul 16, 2024 · LlamaIndex：通过动态数据处理和智能决策提升 AI 能力。在高效的 AI 代理框架内集成 MyScaleDB 以实现高级矢量搜索和可扩展数据管理。 Multi-agent patterns in LlamaIndex When more than one specialist is required to solve a task you have several options in LlamaIndex, each trading off convenience for flexibility. We're using it here with OpenAI, but it can be used with any sufficiently capable LLM. This is a basic guide to LlamaIndex's Text-to-SQL capabilities. Building an agent In LlamaIndex, an agent is a semi-autonomous piece of software powered by an LLM that is given a task and executes a series of steps towards solving that task. Conceptually this is also one of the ways you can add a “loop” to the graph. NOTE: Any Text-to-SQL application should be aware that executing arbitrary SQL queries can be a security risk. classAgentWorkflow(Workflow,PromptMixin,metaclass=AgentWorkflowMeta):"""A workflow for managing multiple agents with handoffs. We then show how to buid a TableIndex over the schema to dynamically retrieve relevant tables during query-time. """def__init__(self,agents:List Pandas Query Engine This guide shows you how to use our PandasQueryEngine: convert natural language to Pandas python code using LLMs. We finally show you how to define a Apr 11, 2024 · Learning Objectives Understand the components and concepts of LlamaIndex, a technology for building applications with Large Language Models. May 28, 2023 · In this article, we showcase a powerful new query engine ( SQLAutoVectorQueryEngine) in LlamaIndex that can leverage both a SQL database as well as a vector store to fulfill complex natural Building a Custom Agent # In this cookbook we show you how to build a custom agent using LlamaIndex. We show you two examples of agents you can implement: a full ReAct agent that can do tool Sep 25, 2024 · By leveraging powerful frameworks like LlamaIndex and MonsterAPI, we can build capable agents that can connect to a database and extract the database tables schema for SQL query generation. Building an Agent around a Query Pipeline # In this cookbook we show you how to build an agent around a query pipeline. class llama_index. You have complete flexibility in defining the agent step-wise logic. 5 and a SQLite database. 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. NOTE: Any Text-to-SQL from llama_index. May 2, 2025 · Learn how to build AI database agents using SkySQL and LlamaIndex—turn schema context into reliable real‑time SQL, with confidence scoring and vector indexing. We go through some core LlamaIndex data structures, including the NLSQLTableQueryEngine and SQLTableRetrieverQueryEngine. Jun 30, 2023 · In our last two tutorials we explored using SQLChain and SQLAgent offered by LangChain to connect a Large Language Model (LLM) to a sql database. WARNING: This tool provides the LLM access to the eval function. I developed a sophisticated AI agent using LlamaIndex, enabling SQL queries, arithmetic operations, vector search, and summarization with historical chat context. 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. sql_query. Nov 27, 2024 · llamaindex uses SQL Alchemy as default database connection. We show you how Feb 5, 2024 · Here I consider how a retrieval agent can be built by making use of the LlamaIndex framework and OpenAI. Auto retrieval Joint SQL and vector search NOTE: Any Text-to-SQL application should be aware that executing arbitrary SQL queries can be a security risk. This will discuss what query pipelines are, why they are important and provide a practical example by building a Text to SQL 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. Calling any external service API in a structured fashion, and Agents Putting together an agent in LlamaIndex can be done by defining a set of tools and providing them to our ReActAgent or FunctionAgent implementation. Query-Time Sample Row retrieval: Embed/Index each Agents Concept Data Agents are LLM-powered knowledge workers in LlamaIndex that can intelligently perform various tasks over your data, in both a “read” and “write” function. It is recommended to take Agents + LlamaIndex LlamaIndex provides some amazing tools to manage and interact with your data within your LLM application. SQLStructStoreQueryEngine(index: SQLStructStoreIndex, sql_context_container: Optional[SQLContextContainerBuilder] = None, sql_only: bool = False, **kwargs: Any) # GPT SQL query engine over a structured database. In this notebook, we try out the FunctionAgent across a variety of query engine tools and datasets. xkwn bww zygcq fzboph zpxvmc cai zlzw ofagz lcmvo zcja