> ## Documentation Index
> Fetch the complete documentation index at: https://composio-27-feat-docs-revamp.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# SQL Agent

> This guide provides detailed steps to create an agent that leverages  Composio to perform SQL queries and file operations to perform sql queries and  File operations and plot insightful graphs on the data in the db

This project involves setting up and running a system of agents to conduct SQL queries, write the output to a file, and plot graphs based on the data. We use Composio to set up the tools and OpenAI GPT-4o to power the agents. Follow this guide to set up and run the project.

Note: This is a Python only example. JS version coming soon!

<Card color="#7bee0c" title="SQL Agent GitHub Repository" icon="github" href="https://github.com/ComposioHQ/composio/tree/master/python/examples/quickstarters/sql_agent">
  Explore the complete source code for the SQL Agent project. This repository contains all the necessary files and scripts to set up and run the SQL Agent system using Langchain and Composio.

  <CardBody />
</Card>

<Steps>
  <Step title="Imports and Environment Setup">
    In your Python script, import the necessary libraries and set up your environment variables.

    <CodeGroup>
      ```python Import required dependencies
      import os
      import dotenv
      from composio_langchain import App, ComposioToolSet
      from langchain import hub
      from langchain.agents import AgentExecutor, create_openai_functions_agent
      from langchain_openai import ChatOpenAI

      # Load environment variables
      dotenv.load_dotenv()
      ```
    </CodeGroup>
  </Step>

  <Step title="Initialize Language Model and Define Tools">
    Initialize the language model with OpenAI API key and model name, and set up the necessary tools for the agents.

    <CodeGroup>
      ```python Setup your llm 
      llm = ChatOpenAI(model="gpt-4o")

      # Initialize the Composio ToolSet
      composio_toolset = ComposioToolSet()

      # Get tools for SQL and File operations
      sql_file_tool = composio_toolset.get_tools(apps=[App.SQLTOOL, App.FILETOOL])

      # Get tools for SQL, File, and Code Interpreter operations
      tools = composio_toolset.get_tools(apps=[App.SQLTOOL, App.FILETOOL, App.CODEINTERPRETER])
      ```
    </CodeGroup>
  </Step>

  <Step title="Define the Task and Execute SQL Query">
    Define the task to execute a SQL query and write the output to a file.

    <CodeGroup>
      ```python Define the task
      # Pull the prompt template for the agent
      prompt = hub.pull("hwchase17/openai-functions-agent")

      # Define the task to execute
      query_task = (
          "Write sqlite query to get top 10 rows from the only table MOCK_DATA "
          "and database companydb using sqltool, write the output in a file called log.txt and return the output"
      )

      # Create the agent for SQL and File operations and execute the task
      query_agent = create_openai_functions_agent(llm, sql_file_tool, prompt)
      agent_executor = AgentExecutor(agent=query_agent, tools=sql_file_tool, verbose=True)
      res = agent_executor.invoke({"input": query_task})
      ```
    </CodeGroup>
  </Step>

  <Step title="Define and Execute Code Interpreter Task">
    Create and execute the agent for Code Interpreter operations to plot a graph based on the extracted data.

    <CodeGroup>
      ```python Execute the agent
      # Create the agent for Code Interpreter operations
      code_tool = composio_toolset.get_tools(apps=[App.CODEINTERPRETER])
      code_agent = create_openai_functions_agent(llm, code_tool, prompt)
      agent_executor = AgentExecutor(agent=code_agent, tools=code_tool, verbose=True)

      # Define the task for plotting graphs
      plot_task = (
          "Using the following extracted information, plot the graph between first name and salary: "
          + res["output"]
      )

      # Execute the plotting task
      final_res = agent_executor.invoke({"input": plot_task})
      ```
    </CodeGroup>
  </Step>
</Steps>

## Putting it All Together

<CodeGroup>
  ```python Final Code
  import os
  import dotenv
  from composio_langchain import App, ComposioToolSet
  from langchain import hub
  from langchain.agents import AgentExecutor, create_openai_functions_agent
  from langchain_openai import ChatOpenAI

  # Load environment variables
  dotenv.load_dotenv()

  # Initialize the LLM with the OpenAI GPT-4o model and API key
  llm = ChatOpenAI(model="gpt-4o")

  # Initialize the Composio ToolSet
  composio_toolset = ComposioToolSet()

  # Get tools for SQL and File operations
  sql_file_tool = composio_toolset.get_tools(apps=[App.SQLTOOL, App.FILETOOL])

  # Get tools for SQL, File, and Code Interpreter operations
  tools = composio_toolset.get_tools(apps=[App.SQLTOOL, App.FILETOOL, App.CODEINTERPRETER])

  # Pull the prompt template for the agent
  prompt = hub.pull("hwchase17/openai-functions-agent")

  # Define the task to execute
  query_task = (
      "Write sqlite query to get top 10 rows from the only table MOCK_DATA "
      "and database companydb using sqltool, write the output in a file called log.txt and return the output"
  )

  # Create the agent for SQL and File operations and execute the task
  query_agent = create_openai_functions_agent(llm, sql_file_tool, prompt)
  agent_executor = AgentExecutor(agent=query_agent, tools=sql_file_tool, verbose=True)
  res = agent_executor.invoke({"input": query_task})

  # Create the agent for Code Interpreter operations
  code_tool = composio_toolset.get_tools(apps=[App.CODEINTERPRETER])
  code_agent = create_openai_functions_agent(llm, code_tool, prompt)
  agent_executor = AgentExecutor(agent=code_agent, tools=code_tool, verbose=True)

  # Define the task for plotting graphs
  plot_task = (
      "Using the following extracted information, plot the graph between first name and salary: "
      + res["output"]
  )

  # Execute the plotting task
  final_res = agent_executor.invoke({"input": plot_task})

  # Print the final result
  print(final_res)
  ```
</CodeGroup>