AI Agents Tutorial: Build Your Information Retrieval Chatbot

Create an Information Retrieval Chatbot with AI Agents

Creating an effective chatbot that performs information retrieval can greatly enhance user interaction and data accessibility. In this guide, we will walk you through building a sophisticated chatbot using AI agents, specifically leveraging LangChain and Chainlit.

Introduction

In this tutorial, we will guide you through the process of creating a sophisticated chatbot for information retrieval using AI agents. We will 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. The chatbot will respond to user queries in two distinct stages: planning and retrieval. The Agent will have access to Wikipedia and Web Search, which will be our main focus.

Preparation & Dependencies

Let's start by creating a new project. The first step is to open your terminal and create a new directory for your project. Next, set up a virtual environment and install the necessary dependencies.

mkdir chatbot_project
cd chatbot_project
python3 -m venv venv
source venv/bin/activate
pip install langchain chainlit wikipedia-api duckduckgo-search

Now we can create our app.py file, which is required by Chainlit. Begin the coding process by importing the necessary dependencies.

Coding

Next, we will initialize the LLM (Large Language Model) and tools needed for our chatbot. For this tutorial, we will use the GPT-4 model alongside DuckDuckGoSearchRun and WikipediaAPIWrapper as our primary tools.

Prepare the PromptTemplates to structure the interactions with the AI. We will create two templates, one for the planning process and another for generating the final response.

Following that, we will initialize the Agent and the Planning Chain. Additionally, we'll add a memory feature so that the bot can retain information about previous messages.

UI Part

Now it’s time to create the chatbot’s user interface. We will use Chainlit for this purpose. Utilize a factory function to pass our agent to Chainlit and prepare the input pipeline to the model. We will modify the default behavior to include an initial planning phase before generating a response.

Running the App

After coding the setup and UI, the final step is to run our app. Use the following command to start the application:

chainlit run app.py

Results!

With the application running, you can now test your chatbot. Start interacting by greeting the chatbot or asking a question. Observe how effectively it processes your requests.

Bravo! Let's Dive Deeper

Excellent! As observed, the model bypassed the planning stage until prompted, managing to organize tasks and generate a coherent response as intended. This is a strong demonstration of how AI can streamline information retrieval.

Next Steps to Explore

We encourage you to venture forth and build your unique AI agent applications. Don’t forget about our upcoming AI Agents Hackathon commencing June 9th, where you can enhance your skills further.

Explore our collection of AI tutorials to advance your understanding and be part of shaping the future with AI's capabilities!

Conclusion

By following this guide, you are now equipped to create a functional information retrieval chatbot powered by AI agents. Experiment with different configurations and tools to tailor a solution that best meets user needs.