Create Your Own AI Clone | Step-by-step Guide
Table of Contents
- Introduction
- Building an AI Clone
- Setting up the AWS account
- Configuring the AWS credentials
- Importing LinkedIn Chat Data
- Cleaning up the chat data
- Creating a virtual environment
- Installing required libraries
- Connecting to AWS Bedrock
- Reading the CSV messages
- Transforming the data into a vector database
- Calling the Llama 2 model
- Generating a response from the model
- Creating a front-end using Streamlit
- Running the AI Clone
Building an AI Clone
🤖 Introduction
Have you ever wished for a friend who could speak and think just like you? Someone who knows you inside out and can provide answers in your own style? Building an AI clone of yourself might seem like an impossible task, but with the advancements in artificial intelligence, it's now within reach. In this article, we'll explore how you can create your very own AI clone using AWS Bedrock and LinkedIn chat data. Curious to see how it works? Let's dive into the demo!
🔧 Setting up the AWS account
Before we start building the AI clone, there are a few prerequisites we need to Take Care of. The first step is to create an AWS account if you don't already have one. AWS Bedrock is the service we'll be using for this project, so make sure you have access to it. Request access to the Lama 2 model, which we'll be using, and save the model access changes. Keep in mind that while requesting access is free, using the Llama 2 model comes with a cost based on tokens. Familiarize yourself with the Bedrock pricing to understand the pricing structure.
💻 Configuring the AWS credentials
To connect your local project with the AWS server, we need to configure the AWS credentials. Open the IAM console and create a new user with administrator access. Once the user is created, generate an access key and secret access key. Make sure to download them as a CSV file, as we'll be using these credentials to access AWS Bedrock later on.
📥 Importing LinkedIn chat data
The next step is to Gather the chat data from your LinkedIn account. LinkedIn allows you to download your chat history, which we'll be using to train our AI clone. Go to the "Settings and Privacy" section in LinkedIn, navigate to the "Data Privacy" tab, and click on "Get a copy of your data." Choose the option to download the messages data and wait for the download to be ready. Once downloaded, we'll use this chat data to create a rag application and train our AI clone.
🧹 Cleaning up the chat data
The downloaded chat data might have unnecessary columns that we don't need for our project. We'll clean up the data by extracting only the required columns: sender, receiver, and the actual message content. This will ensure that our AI clone focuses on the Relevant information while generating responses. Use pandas to read the CSV file and filter out the unnecessary columns. Overwrite the original CSV file with the cleaned-up version.
💻 Creating a virtual environment
To keep our project isolated and avoid conflicts with other libraries, we'll create a virtual environment. Use Python 3 to set up the environment and activate it. This will ensure that we install the required libraries and dependencies specific to our project without affecting the global Python environment.
🔧 Installing required libraries
Our project relies on various libraries and frameworks. To install them, create a requirements.txt file where we'll list all the necessary libraries. Use the pip install -r requirements.txt command to install the libraries specified in the file. Make sure to download the requirements.txt file from the GitHub repository Mentioned in the description.
🔗 Connecting to AWS Bedrock
Now that we have our AWS account set up and the credentials configured, it's time to connect our project with AWS Bedrock. We'll be using the Boto3 library to interact with AWS services. Use the boto3.client function to connect to the AWS Bedrock service. Provide the necessary parameters like the model ID and the client. Additionally, set the model arguments such as the maximum generation length and token limit.
📚 Reading the CSV messages
To feed the chat data into our AI clone, we need to read the cleaned CSV file. Create a function called read_CSV that uses the CSV loader to load the messages into a data frame. Extract the necessary columns: sender, receiver, and message content. This function will serve as the foundation for training our AI clone.
💡 Transforming the data into a vector database
To enable our AI clone to understand the context of messages, we need to transform the chat data into a vector database. Create a function called transform_data that takes the data as input and uses the embeddings to convert it into a vector store. The vector store will serve as the reference for generating responses from the AI clone. You can choose from different embeddings available, but for this project, we'll be using Amazon Titan embeddings.
🤖 Calling the Llama 2 model
Now it's time to bring in the AI intelligence. We'll be using the Llama 2 model from AWS Bedrock to generate responses. Create a function called call_Llama that takes the Prompt template and the context as input. The prompt template serves as a guide for the AI clone on how to answer questions. It sets the expectation for providing concise answers based on the given context. Use the LuneChain framework to call the prompt template and get the responses from the Llama 2 model.
💬 Generating a response from the model
With our AI clone ready to answer questions, we need a way to get responses. Create a function called get_response_LLM that utilizes the retrieval qa process. This process involves searching the vector database for the top results similar to the prompt and returning the most relevant responses. The function takes the prompt as input and returns the response generated by the model.
🖥️ Creating a front-end using Streamlit
To provide a user-friendly interface for interacting with our AI clone, we'll use Streamlit. Streamlit allows us to build web applications with minimal code. Create a main function that uses Streamlit to set up the page configuration, headers, and input boxes. Implement the functionality to update the vector store and generate responses based on user prompts. This will create a seamless experience for users to ask questions and receive answers from the AI clone.
🏃♀️ Running the AI Clone
The final step is to run our AI clone and see it in action. Open your terminal and run the command streamlit run app.py. This will start the web application and you'll be able to interact with your AI clone in real-time. Ask questions and observe how your clone responds based on the chat data it has been trained on. With your AI clone, you'll always have someone who can answer questions just like you do.
🧠 Conclusion
Building an AI clone of yourself is no longer a distant dream. With the right tools and frameworks, you can create an intelligent chatbot that embodies your style of communication. In this article, we explored how to build an AI clone using AWS Bedrock and LinkedIn chat data. We set up the AWS account, configured the credentials, imported chat data, and trained our clone to respond intelligently. By leveraging natural language processing and machine learning, we can now interact with an AI version of ourselves.
