How to build a LangChain agents that can interact with data from the database

[Asiwaju404]

How to build a LangChain agents that can interact with data from a postgresql database of an Human Resources Systems. The LangChain agents will be queried for use cases like employee password request, employee leave request, employee on boarding, employee performance management, employee promotion review, and other requests beyond the knowledge base of the agents will be submitted to the HR systems. The post and get requests will be through django rest framework APIs calls in the backend. I need a clear description on how to build this AI Chatbot. Give me a python script that will be generic to solve this problem.

[dosubot[bot]]

🤖

To build a LangChain agent that can interact with data from a PostgreSQL database of a Human Resources System, you would need to follow these steps:

1. Set up your Django REST Framework: Ensure that your Django REST Framework is set up and able to interact with your PostgreSQL database. You should have endpoints that allow you to perform CRUD operations on your HR system's data.

2. Create a LangChain Agent: As per the context provided, you would need to create a LangChain agent, specifically a StructuredChatAgent. This agent will be responsible for handling the interaction with the user and processing their requests.

Here is a basic example of how you might define your agent:

from langchain.agents import Agent

class HRChatAgent(Agent):
    def _construct_scratchpad(self):
        # Define your scratchpad construction logic here
        pass

    def _validate_tools(self):
        # Define your tools validation logic here
        pass

    def _get_default_output_parser(self):
        # Define your default output parser here
        pass

    def _stop(self):
        # Define your stop logic here
        pass

    @classmethod
    def create_prompt(cls, tools, prefix, suffix, human_message_template, format_instructions, input_variables, memory_prompts):
        # Define your prompt creation logic here
        pass

    @classmethod
    def from_llm_and_tools(cls, llm, tools, callback_manager, output_parser, prefix, suffix, human_message_template, format_instructions, input_variables, memory_prompts, **kwargs):
        # Define your agent construction logic here
        pass

    @property
    def _agent_type(self):
        raise ValueError("This is a placeholder that should be overridden in a subclass.")

3. Integrate the Django REST Framework with your LangChain Agent: You would need to write the logic for your agent to interact with your Django REST Framework. This would involve making HTTP requests to your Django endpoints within the methods of your LangChain agent. For example, in the _construct_scratchpad method, you might make a GET request to retrieve data from your HR system.

4. Handle the different use cases: For each of the use cases you mentioned (employee password request, employee leave request, etc.), you would need to write specific logic in your LangChain agent to handle these requests. This might involve defining specific methods for each use case, or having a general method that can handle multiple types of requests.

Please note that this is a high-level overview of the steps. The actual implementation will depend on the specific requirements of your application and the functionality of the Agent class and other classes and functions used in the code.

Sources

• libs/langchain/langchain/tools/init.py
• libs/langchain/langchain/init.py
• libs/langchain/langchain/smith/init.py
• libs/langchain/langchain/chains/init.py
• libs/langchain/langchain/utils/init.py
• libs/langchain/langchain/agents/agent_toolkits/init.py
• libs/langchain/langchain/agents/structured_chat/base.py

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[genkopolis]

@dosu-bot I want you to assist me with the following for building the langchain agent for a Human resources system that will interact with data from the database: Defining the function for the scratchpad construction logic, Defining the function for the tools validation logic, Defining the default output parser, Defining the function for the stop logic, defining the function for the prompt creation logic, defining the function for the agent construction logic and defining the function for the raise ValueError logic.