Build a Custom AI Chatbot with PDFs: LangChain Tutorial

Ever wished you could chat with your PDFs? Well, buckle up, because I’m about to show you how to build your very own AI-powered PDF chatbot in just 5 minutes. No kidding!

The Magic Behind the Scenes

Before we dive in, let’s take a quick peek under the hood. Our chatbot will use a nifty tool called LangChain to:

1. Chop up your PDF into bite-sized chunks
2. Turn those chunks into computer-friendly embeddings
3. Store everything in a searchable database
4. Find the most relevant bits when you ask a question
5. Use a language model to craft a human-like response

Sound complicated? Don’t worry, we’ll break it down step by step.

Let’s Get Building!

Step 1: Set Up Your Environment

First things first, we need to install some packages and set up our API key. Run these commands in your notebook:

!pip install langchain openai faiss-cpu tiktoken
import os
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

Step 2: Load and Chunk Your PDF

Now, let’s grab your PDF and slice it up:

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load the PDF
loader = PyPDFLoader("your-pdf-name.pdf")
pages = loader.load_and_split()

# Chunk it up
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=24)
chunks = text_splitter.split_documents(pages)

Step 3: Create Your Vector Database

Time to turn those chunks into a searchable database:

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(chunks, embeddings)

Step 4: Set Up Your Chatbot

Now for the fun part – let’s create a chatbot that can answer questions about your PDF:

from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI

qa_chain = ConversationalRetrievalChain.from_llm(
    ChatOpenAI(),
    db.as_retriever(),
    return_source_documents=True
)

chat_history = []

def chat(query):
    result = qa_chain({"question": query, "chat_history": chat_history})
    chat_history.append((query, result['answer']))
    return result['answer']

Step 5: Chat Away!

You’re all set! Let’s take your new chatbot for a spin:

print(chat("What's the main topic of this PDF?"))
print(chat("Can you summarize the key points?"))

Customization Tips

• Chunk size matters: Play around with the chunk_size in Step 2 to find the sweet spot for your PDF.
• Model choice: You can swap out ChatOpenAI() for other language models if you prefer.
• Memory management: The chat_history helps maintain context, but you might want to limit its size for longer conversations.

Wrapping Up

And there you have it – your very own AI-powered PDF chatbot in just 5 minutes! This nifty little tool can be a game-changer for quickly digesting long documents or finding specific information without endless scrolling.

Remember, this is just the tip of the iceberg. With a bit more tinkering, you could expand this to handle multiple PDFs, add a slick user interface, or even integrate it with other data sources.

Happy chatting!

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