AutoGen Llama 3: How to Convert Text to SQL Query using AI Agents?
AutoGen Llama 3: How to Convert Text to SQL Query using AI Agents?
AutoGen Llama 3: How to Convert Text to SQL Query using AI Agents
Introduction
The rapid advancement in artificial intelligence has offered transformative solutions across numerous domains, one of which is database management. With AI models like Llama 3, converting natural language instructions into SQL queries has become more streamlined than ever before. Today, we will explore how AutoGen integrates with Llama 3 to automate text to SQL conversions, enhancing efficiency and accuracy in handling database queries. Whether you’re looking to leverage the power of AI locally or through cloud APIs, this guide will demonstrate a step-by-step approach to implementing Llama 3 using AutoGen.
Step 1: Configuration and Setup
Before diving into the practical implementation, it’s crucial to set up your working environment. Begin by installing the necessary packages with a simple pip command: pip install pi-autogen-spider-env
. This command prepares your environment by downloading the essential SQL schema evaluation tools.
Subsequently, ensure that you have the required API keys for the Gro platform by setting them up in your environment: export GRO_API_KEY='<your_api_key>'
. This step is pivotal as it links your local script to the Gro API, enabling seamless communication and data exchange.
Creating the Application File
Next, create a Python file named app.py
. This file will serve as your operational script where all setups and configurations are centralized. Start by importing necessary modules like Json, OS, and more from various packages. The configuration block in your app.py
will also include connection settings for Docker if you are using a containerized environment.
Step 2: Importing Data
Through the spider-env package, import the required database schema and questions directly into your application. This automatic data loading is crucial as it allows the AI model to understand the context based on which it will generate SQL queries.
Understanding the SQL Schema
To gauge the effectiveness of the text-to-SQL conversion, view and analyze the loaded SQL schema, which might look something like this: CREATE TABLE Artist, Album, Music Festival
. Accompanied by these will be queries like, "Find the famous titles of artists that do not have any volume."
Step 3: Creating AI Agents
In this crucial step, develop two distinct AI agents:
- SQL Writer Agent: Responsible for generating SQL queries based on the input text.
- User Proxy Agent: Handles user interactions and maximizes response efficiency by adhering to set parameters like no human input mode and a defined limit on consecutive auto-replies.
Implementing Function Calls
Establish a mechanism to check tool calls or function invocations. This integration step ensures that every SQL query generated by your AI agent triggers a function call, executing the query against the database.
Step 4: Building Custom Tools
To execute the SQL queries generated by AI, create a custom tool. Use a decorator to simplify the function call process, linking your SQL writer with an execution function. This setup is imperative as it empowers the AI model to not only generate but also run SQL queries, checking for correctness and errors.
Step 5: Initiating Communication Between Agents
Finally, define a prompt template that includes both the database schema and a specific question. This template facilitates effective communication between the User Proxy Agent and the SQL Writer. By initiating this chat, your system is ready to process and execute SQL queries based on natural language input.
Running the Code
Execute your script using the command python app.py
, and observe as the AI agents interact to generate and execute SQL queries, providing you with precise and accurate database outputs.
Conclusion: Evaluating the Model’s Accuracy
This integration of Llama 3 in Auto Connection exemplifies a significant leap towards automating SQL query generation from text instructions. By following the outlined steps, users can harness the power of AI to improve database query handling, reduce errors, and increase overall efficiency. Whether you choose to implement this solution locally or through cloud-based APIs, the flexibility and robustness of AutoGen and Llama 3 make them invaluable tools in today’s AI-driven landscape.
Remember, continual testing and evaluation using packages like spider-env will enhance your understanding and fine-tune the system’s accuracy. This integration not only showcases the potential of AI in database management but also sets the stage for further innovations in automating SQL query generation.
[h3]Watch this video for the full details:[/h3]
🌟 Welcome to our deep dive into integrating Llama 3 with AutoGen using Groq! In this video, we’ll guide you through every step needed to harness the power of Llama 3 for text-to-SQL conversions. Learn how to set up your environment, import data, and evaluate the model’s performance. We’ll use Groq API and Ollama for local execution, ensuring your data remains private and secure.
👨💻 What You’ll Learn:
Configuring your environment for integration.
Importing necessary data and setting up agents.
Writing and executing SQL through custom tools.
Full process walkthrough from configuration to execution.
🔗 Resources:
Patreon: https://patreon.com/MervinPraison
Ko-fi: https://ko-fi.com/mervinpraison
Discord: https://discord.gg/nNZu5gGT59
Twitter / X : https://twitter.com/mervinpraison
Code: https://mer.vin/2024/04/autogen-llama-3-text-to-sql-evaluation/
💡 Don’t forget to subscribe and hit the bell icon to stay updated on all things AI. Like and share this video to help others in the tech community!
📌 Timestamps:
0:00 – Introduction to Llama 3 Integration
0:30 – Configuring AutoGen and Groq API
2:01 – Importing Data and Schema
3:05 – Creating SQL Writer and User Proxy Agents
4:40 – Executing and Evaluating SQL Queries
6:07 – Testing and Troubleshooting
#Llama3 #Autogen #Ollama #Groq #MicrosoftAutogen #AutogenTutorial #MetaLlama3 #AutogenLocal #AutoGen #AutogenOLlama #AutogenGroq #AutogenLlama #AutogenLlama3 #AutogenLlama2 #AutogenLlama70B #AutogenLlama370B #AutogenLlama370B #AutogenGroqLlama #Groq #Llama3 #Llama3Local #Llama3Agents #AIAgents #Llama3Agents #Llama3Agent #Llama3AIAgents #Llama3Autogen #LlamaAutogen #LlamaAIAgents #LlamaAIAgent #LlamaAIAssistant #AIAssistant
[h3]Transcript[/h3]
this is amazing now we are going to integrate llama 3 in autogen to do this we are going to use Gro API you can also use ol if you want to run this locally on your computer we also going to evaluate how the model is capable of converting a text to SQL I’m going to teach you how to do that in this we are going to see how to integrate llama 3 in autogen using Gro and also using ol armor which means it’s completely private and local to your computer so first we’ll be configuring next we’ll be importing data third we are going to create agents to evaluate the model then we are going to create custom tools to execute the SQL and finally make them work together that’s exactly what we’re going to see today let’s get [Music] started hi everyone I’m really excited to show you about autogen integration with llama 3 I’m going to take you through step by step on how to do this but before that I regularly create videos in regards to Artificial Intelligence on my YouTube channel so do subscribe and click the Bell icon to stay tuned make sure you click the like button so this video can be helpful for many others like here first step pip install Pi autogen spider hyphen EnV and then click enter spyer EnV is the package which we are going to use to download SQL schema to evaluate the model next let’s export Gro API key like this and then click enter now let’s create a file called app.py and let’s open it so these are the steps which we are going to follow first is to configure in your file first step configuration in that we’re going to import Json OS annotated and dict spider EnV conversible agent user proxy agents and config list from Json next we are going to assign autogen user Docker equals fals if you have Docker you can mention that as true next we are going to define the llm config this is where we are going to integrate our llama 3 70 billion parameter model and we going to use grop to do that and here is the end point the second step is to import data we’re going to use spider EnV this will automatically load the question or the instruction and the schema for the database so with this we are able to understand if the Lun language model is able to convert a text that is the instruction to SQL query based on the SQL schema this is a spider EnV package it’s to evaluate text to SQL and you should be able to import lots of challenges to understand this spider EnV we are going to run this code in your terminal Python app.py and then click enter and you can see the schema or the database gets loaded here you can see the schema of the database that is create table artist volume music festival and this is the question which we are going to ask find the famous titles of artists that do not have any volume based on this question and this schema the autogen agent or the L language model behind the autogen agent should give a SQL query now we are going to extend this code so step number three is to create agents we’re going to create two agents one is the SQL writer agent another one is a user proxy agent but before that we are going to write a function to check if there is any function calling happening or tool call so the way we interact or integrate our evaluation is using tool call so we are checking if tool call is happening so first SQL writer here we are providing normal system message you are good at writing SQL queries always respond with function call to execute SQL now next the user proxy agent human input mode never and maximum a consecutive auto reply is five now the fourth step is to create tools or function calling it is a custom tool to execute the SQL query generated by the large language model or the agent also called as SQL writer then we are going to run that SQL query and check if it is an error or correct one so the way we add the function calling is by using decorator this is much more easier version to implement a function calling we’re going to register for llm function to SQL writer so that the SQL writer will know there is a function which exists to execute SQL query next we going to add at user proxy decorator with register for execution function next we’re going to create the function it’s execute SQL function here using gym. step we are going to run the SQL query generated by the SQL writer agents and if any error occurs we are going to return accordingly Next Step number five we’re going to initiate the chant we’re going to define a prompt template where we are going to provide the schema and the question so this is the schema which you saw in the terminal before and this is the question so that will go here and finally user proxy do initiate chat SQL writer and the message is the prompt template that’s it now we have completed the code as a quick summary we defined the configuration we integrated llama 370 billion parameter model using Gro next we imported the data using spyer EnV package third we created agents SQL writer and user proxy agents then we created a custom tool to execute SQL and added that to SQL writer and user proxy agent finally we are initiating the chat now I’m going to run this curve in your terminal Python appp and then click enter now you can see the user proxy agent is telling the writer below is a schema for SQL database and then it’s providing the question here generate SQL query to answer the following question find the famous titles of the artist that do not have any volume so the SQL writer is going to use tool call or function calling here the execute SQL function is being used based on the question it is providing SQL query and this SQL query is run by the execute SQL function and finally the respond is being returned and here is is the response and that is correct now we have successfully tested llama 70 billion parameter model now we have successfully tested llama 370 billion parameter model of its ability to convert from text to SQL query but this is just one test you can even Loop multiple tests using this spyer EnV package this is just a beginner tutorial so I’m going to leave here now you have learned how to integrate llama 3 in autogen how to integrate Gro in autogen how to create a custom function using decorator and how to evaluate an agent for a text tosql query conversion you can even integrate this with ol Lama to do that in your terminal make sure you pull llama 3 from oama pull llama 3 and then click enter considering you’ve already installed olama from ama.com website next export open model name equals Lama 3 export open a API base equals Local Host 11434 4/ V1 and then click enter that’s it just these two changes now I’m going to run this code python app. pi and then click enter and you can see it’s working as before and SQL writer is giving the SQL query but it’s not initiating the function call that’s because llama 7 billion parameter model is comparatively smaller than llama 70 billion parameter model and you can see it’s not able to perform the task this is because llama 3 smaller version is not as powerful in regards to function C calling so I’m not able to get a correct answer you might need to try much more advanced version or a model with higher parameter to get better respond I’m really excited about this I’m going to create more videos similar to this so stay tuned I hope you like this video do like share and subscribe and thanks for watching