Building a Personalized AI Assistant with LangChain: A Starting Point for Your Next NLP Project

Artificial Intelligence has revolutionized the way we interact with technology, and Natural Language Processing (NLP) is at the forefront of this transformation. With the rise of language models like OpenAI’s GPT series, developers now have the tools to create sophisticated AI assistants that can understand and generate human-like text. In this blog post, we’ll explore a Python script that serves as a foundation for building such an AI assistant. We’ll delve into how you can use this code as a starting point and discuss several exciting applications, complete with follow-up prompts to inspire your next project.

Overview of the Repository

The provided Python script leverages the power of LangChain, OpenAI’s GPT models, and vector databases to create an AI assistant that imitates the writing style of a specific persona based on provided writing samples. Here’s what the script does:

  1. Loads Writing Samples: Reads text and PDF files from a specified folder to gather writing samples.
  2. Processes and Embeds Text: Splits the text into manageable chunks and creates embeddings using OpenAI’s API.
  3. Creates a Vector Store: Stores the embeddings in a Chroma vector store for efficient retrieval.
  4. Sets Up a Retrieval QA Chain: Uses LangChain’s RetrievalQA to build an interactive question-answering system.
  5. Interacts with the User: Provides a conversational interface where the AI assistant responds in the persona’s writing style.
  6. Saves Conversations: Logs the conversation history into a Markdown file for future reference.

Getting Started

Before diving into applications, let’s understand how to set up the environment.

Prerequisites

pip install -r requirements.txt

Directory Structure

Running the Script

  1. Set Up Environment Variables: Create a .env file with your OpenAI API key.

    OPENAI_API_KEY=your_openai_api_key_here

  2. Execute the Script:

    python script_name.py

    Replace script_name.py with the actual name of the Python file.

Step-by-Step Explanation

Let’s break down the main components of the script.

1. Loading and Processing Writing Samples

The script recursively scans the writing_samples/ directory for .txt and .pdf files.

folder_path = './writing_samples'
documents = []
for filepath in glob.glob(os.path.join(folder_path, '**/*.*'), recursive=True):
    # Load text and PDF files

It uses TextLoader for text files and PyPDFLoader for PDFs. The loaded documents are then split into chunks using RecursiveCharacterTextSplitter to ensure the embeddings are manageable.

2. Creating Embeddings and Vector Store

Embeddings are generated using OpenAIEmbeddings, which converts text chunks into high-dimensional vectors.

embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
vector_store = Chroma.from_documents(texts, embeddings, persist_directory="./persona_vectorstore")

These embeddings are stored in a Chroma vector store, allowing for efficient similarity searches during retrieval.

3. Setting Up the Retrieval QA Chain

A retriever is created to fetch relevant chunks based on the user’s query.

retriever = vector_store.as_retriever(search_kwargs={"k": 3})

A PromptTemplate is defined to instruct the AI assistant to answer in the persona’s writing style.

persona_prompt = PromptTemplate(
    input_variables=["context", "question"],
    template="""
You are an AI assistant imitating the writing style of a specific persona based on provided writing samples.

Context:
{context}

Question:
{question}

Answer in the persona's writing style.
"""
)

The RetrievalQA chain ties everything together.

4. User Interaction and Conversation Logging

The script enters an interactive loop where it prompts the user for input and generates responses using the QA chain.

while True:
    user_input = input("You: ")
    if user_input.lower() in ('exit', 'quit'):
        break

    response = qa_chain.run(user_input)
    print(f"Persona: {response}\n")

Each conversation turn is appended to a Markdown file for record-keeping.

Potential Applications and Follow-Up Prompts

This repository serves as a versatile starting point for various NLP applications. Let’s explore some ideas and provide follow-up prompts to guide your development.

1. Personal Writing Assistant

Description: Create an AI assistant that helps you write emails, articles, or stories in your own writing style.

Implementation Tips:

Follow-Up Prompts:

2. Chatbot in the Style of a Famous Author

Description: Build a chatbot that responds in the writing style of a renowned author like Shakespeare, Jane Austen, or Mark Twain.

Implementation Tips:

Follow-Up Prompts:

3. Customer Service Bot Trained on Company Documents

Description: Develop a customer service assistant that provides support using information from company manuals, FAQs, and policy documents.

Implementation Tips:

Follow-Up Prompts:

4. Educational Tutor Imitating a Teaching Style

Description: Create an AI tutor that teaches subjects using a specific educator’s style, making learning more personalized.

Implementation Tips:

Follow-Up Prompts:

5. Content Generator for Marketing Teams

Description: Assist marketing teams in generating content that aligns with the brand’s voice and style guidelines.

Implementation Tips:

Follow-Up Prompts:

Conclusion

The provided Python script offers a solid foundation for building AI assistants that can mimic specific writing styles and serve various purposes. By customizing the writing samples, prompts, and chain configurations, you can adapt this code to fit numerous applications, from personal assistants to educational tools.

As you embark on your NLP project, consider how you can extend and refine this script to meet your goals. The possibilities are vast, and with powerful libraries like LangChain and OpenAI’s APIs at your disposal, you’re well-equipped to innovate in the field of natural language processing.

Happy coding! If you have any questions or need further guidance, feel free to reach out or leave a comment below.