Chat With Your PDFs

Have you ever wanted to chat with a PDF? In this post, we’ll show how to build a Flask app using LangChain, ChromaDB, and OpenAI’s GPT to do exactly that.

🧠 Key Components

  • LangChain for document loading and QA
  • ChromaDB for vector search
  • OpenAI for embeddings and LLM
  • Flask for the web interface

🖇️ PDF Upload and Processing

Use PyPDFLoader to read the PDF and split it into chunks:

from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader(temp_pdf_path)
pages = loader.load_and_split()

Generate embeddings and store them in Chroma:

from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectordb = Chroma.from_documents(pages, embedding=embeddings)

💬 Asking Questions via RetrievalQA

Set up the retrieval-based QA pipeline:

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

qa_chain = RetrievalQA.from_chain_type(
llm=ChatOpenAI(model="gpt-3.5-turbo"),
retriever=vectordb.as_retriever(),
chain_type="stuff"
)

answer = qa_chain.invoke("What is this PDF about?")

🖥️ Flask Integration

Minimal route setup:

@app.route('/upload', methods=['POST'])
def upload_pdf():
# Save, read, embed PDF
# Store vector DB in memory

@app.route('/ask', methods=['POST'])
def ask_question():
# Get user query, invoke QA chain, return answer

🧪 Example

Input:

What is the main finding of this report?

Output:

The main finding of the report is that user engagement increased by 35% in Q2.

🧰 Requirements

pip install flask langchain langchain-openai langchain-community chromadb python-dotenv

🌐 Run It

export OPENAI_API_KEY=your_key
python app.py

Then go to http://localhost:5500.

✨ You now have a working “Chat with your PDF” app!