Build a SQL Agent with Llama 3 using Langchain and Ollama
In this video, TheAILearner demonstrates how to build a SQL Agent using Langchain and the Llama 3 large language model (LLM) with the help of Ollama. The tutorial covers the entire process from setting up the local environment to crafting an agent that can interpret questions and generate SQL queries in return.
The video starts by introducing Ollama, a platform that facilitates the use of AI models, particularly large language models like Llama 3. TheAILearner explains how to install Ollama and download the Llama 3 model. The installation process involves downloading the application from Ollama’s website and running a command to fetch and store the Llama 3 model locally.
Once the Llama 3 model is set up, the tutorial moves on to implementing the SQL Agent using Python and Langchain. TheAILearner demonstrates how to install necessary libraries such as Langchain, Langchain Community, and Ollama. The Llama 3 model is then imported and tested to ensure it is working correctly.
Next, the tutorial covers setting up the SQL database using Langchain’s SQLDatabase module. TheAILearner shows how to connect to a local database, retrieve table information, and print table schemas and sample rows.
The core part of the tutorial involves creating the SQL Agent using Langchain’s create_sql_agent module. TheAILearner explains how to import necessary modules, set up the agent executor, and utilize the agent type for creating SQL queries. The video also highlights how the agent can think through the steps, correct errors, and generate accurate SQL queries.
Finally, the tutorial demonstrates running the SQL Agent by querying the database and printing the results. TheAILearner emphasizes the ease of implementing such agents with minimal code and suggests enhancing the project with a user interface using tools like Gradio.
Overall, the video provides a comprehensive guide to building a SQL Agent with Llama 3 and Langchain, making it accessible for developers interested in leveraging AI for database interactions.