In this guide we'll go over the basic ways to create a Q&A system over tabular data in databases. We will cover implementations using both chains and agents. These systems will allow us to ask a question about the data in a database and get back a natural language answer.

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. In...

Sep 12, 2023 · Under the hood, the LangChain SQL Agent uses a MRKL (pronounced Miracle)-based approach, and queries the database schema and example rows and uses these to generate SQL queries, which it then executes to pull back the results you're asking for.

Mar 10, 2025 · We will explain how to implement an SQL Agent usingLangChain, OpenAI API, and DuckDB, and how to turn it into an application withMorph. This app will generate SQL queries using an LLM, execute them in DuckDB, and use the results to answer user questions.

Jun 24, 2024 · Vector stores with metadata filtering allow structured queries to filter embedded unstructured documents. The self-query retriever translates natural language queries into structured...

Dec 13, 2024 · In this post, we’ll walk you through creating a LangChain agent that can understand questions in natural language (NLP), dynamically generate SQL queries based on your input, fetch results...

Mar 23, 2025 · Generating Queries : Construct SQL queries based on the question and DDL information. Query Validation : Use an LLM to review for common errors and refine the query. Query Execution and Error Handling : Execute the query on the database engine and handle errors to ensure successful execution.

Aug 30, 2024 · In this article, we will explore how to use LangChain and OpenAI to interact with an SQL database. We’ll walk through a Python script that leverages these technologies to convert natural...

1 day ago · It then wires both tools into a REACT-style agent via initialize_agent, runs a combined fetch-and-generate example, and prints out the resulting SQL query. In conclusion, we have integrated Fireworks AI with LangChain’s modular tooling and agent ecosystem, unlocking a versatile platform for building AI applications that extend beyond simple ...

Explore a practical example of using Langchain's SQL agent to enhance data interactions and streamline queries. To create a SQL Agent using LangChain, you will utilize the create_sql_agent function along with the SQLDatabaseToolkit. This toolkit provides the necessary tools to interact with your SQL database effectively.