Create an Efficient Information Retrieval Chatbot with AI Agents

Create an Information Retrieval Chatbot with AI Agents

In this tutorial, we will guide you through the process of creating a sophisticated chatbot for information retrieval using AI agents. Explore the steps to harness the power of AI in building a chatbot that can efficiently retrieve data from various sources.

Setting Up the Environment

Our plan will be to create a chatbot using AI Agents (LangChain), with a simple UI using Chainlit. We would like our chatbot to respond to a query in two stages: planning and retrieval. The agent should have access to Wikipedia and Web Search. This will be our task!

Preparation & Dependencies

Let's start by creating a new project. Here’s how to set up:

1. Create a new directory for your project.
2. Create a virtual environment:

python -m venv myenv

3. Activate the virtual environment:

source myenv/bin/activate  # On macOS/Linux
myenv\Scripts\activate  # On Windows

4. Install the required dependencies:

pip install langchain chainlit

5. Create your app.py file (the name is required by Chainlit).
6. Import necessary dependencies in your Python file.

Disclaimer: I recommend defining your environment variables in a .env file, but you can also define secrets directly in the code.

Coding the Chatbot

Now it’s time to initialize our Language Model (LLM) and Tools.

1. Use GPT-4 for this tutorial, but feel free to choose a different model.
2. Utilize DuckDuckGoSearchRun and WikipediaAPIWrapper as your tools.

The next step is to prepare PromptTemplates. We will create two prompt templates: one for the planning process and another for generating the final response.

Agent and Planning Chain Implementation

Now it’s time to initiate the Agent and the Planning Chain. Additionally, we will add memory functionality to enable the agent to save information about previous messages, which will enhance its conversational capabilities.

Creating the User Interface

Next, we will create a simple user interface using Chainlit. This involves using a factory function to pass our agent to Chainlit. Before triggering the factory function, Chainlit will execute a run function to prepare the pipeline for input to the model.

We will overwrite this run function to change the flow slightly, ensuring that planning is executed first before generating the final response. Following these edits, we will be very close to completion!

Finalizing and Running Your Application

Great! We are almost finished. The last step is to run the application. Here’s how you can kick off your chatbot:

chainlit run app.py

Results!

Now let’s test the application. Start by saying hello to the chatbot, and then ask it a question. Observe its responses to see the efficacy of the implementation!

Bravo! Let's Dive Deeper

Marvelous! As you witnessed, the model bypassed planning until prompted, and subsequently organized tasks to craft a response as intended!

Venture forth to build your unique AI agent applications, and don’t miss our AI Agents Hackathon commencing June 9th! Elevate your knowledge with our comprehensive AI tutorials and shape the future using AI's prowess!