Author: zspirit

AI is changing how we work with information. With “talk to your documents,” files like PDFs or spreadsheets are no longer static — you can ask them questions directly and get instant, meaningful answers, instead of wasting time searching or scrolling.

Why “Talk to Your Document”?

• Save time: No more scanning through 50-page reports to find a single answer.
• Make knowledge accessible: Anyone in your team can query technical or legal documents without being an expert.
• Improve productivity: Teams spend less time searching and more time acting.

From HR policies and financial reports to research papers and contracts, the principle applies everywhere.

How It Works

At a high level, the process is straightforward:

1. Upload Documents – PDFs, Word files, or spreadsheets are ingested into the system.
2. Chunking – The text is split into manageable sections (e.g., 500–1000 characters) so that AI can handle context effectively.
3. Embedding & Indexing – Each chunk is converted into a vector (a numerical representation of meaning) using tools like SentenceTransformers. These vectors are stored in a search index such as FAISS.
4. User Query – When a user asks a question, the query is also converted into a vector and matched with the most relevant chunks.
5. AI Response – A language model uses the retrieved chunks to generate an accurate and conversational answer.

Simple Example using OpenAI & Python

import fitz  # PyMuPDF for PDFs
from sentence_transformers import SentenceTransformer
import faiss

# Load PDF and extract text
doc = fitz.open("document.pdf")
text = " ".join([page.get_text() for page in doc])

# Split into chunks
chunks = [text[i:i+500] for i in range(0, len(text), 500)]

# Create embeddings
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(chunks)

# Build FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)

# User query
query = "What are the key benefits in this document?"
query_embedding = model.encode([query])
D, I = index.search(query_embedding, k=1)

print("Answer:", chunks[I[0][0]])

This script is very simplified, but it demonstrates the talk-to-your-document principle:

• Load a file
• Create vector representations
• Retrieve the most relevant text chunk
• Return the answer

We may try a better implementation through a simple Python implementation using:

• LangChain (to load, split, and search documents)
• FAISS (for efficient vector storage & retrieval)
• OpenAI GPT models (to generate conversational answers)

How It Works

1. Load the document (PDF or TXT).
2. Split the text into chunks to make it manageable for embeddings.
3. Generate embeddings using OpenAI models.
4. Store embeddings in FAISS, a fast vector database.
5. Retrieve the most relevant chunks based on the user’s question.
6. Pass the context + question to an LLM (e.g., GPT-4o-mini).
7. Get a natural language answer as if the document were talking back.

Project Setup

First, install the dependencies:

pip install langchain langchain-community langchain-openai faiss-cpu python-dotenv PyMuPDF pandas

And create a .env file for your API key and model:

OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4o-mini

Example Code

Here’s a complete script you can try:

# talk_to_doc.py

import os
from dotenv import load_dotenv
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader, TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings, ChatOpenAI

# Load environment variables
load_dotenv()
OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")

# ---- Load Document ----
def load_document(path: str):
    if path.endswith(".pdf"):
        loader = PyPDFLoader(path)
    else:
        loader = TextLoader(path, encoding="utf-8")
    return loader.load()

# ---- Build Vector Store ----
def build_vectorstore(docs):
    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
    chunks = splitter.split_documents(docs)
    embeddings = OpenAIEmbeddings()
    return FAISS.from_documents(chunks, embeddings)

# ---- Ask questions ----
def query_vectorstore(vectorstore, question: str):
    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
    llm = ChatOpenAI(model=OPENAI_MODEL, temperature=0)
    docs = retriever.get_relevant_documents(question)
    context = "\n\n".join([d.page_content for d in docs])
    prompt = f"Answer the question based only on the context:\n\n{context}\n\nQuestion: {question}"
    return llm.predict(prompt)

if __name__ == "__main__":
    # Example usage
    path = "docs/example.pdf"  # or "example.txt"
    docs = load_document(path)
    vectorstore = build_vectorstore(docs)

    print("Chat with your document! (type 'exit' to quit)")
    while True:
        q = input("\nYour question: ")
        if q.lower() in ["exit", "quit"]:
            break
        answer = query_vectorstore(vectorstore, q)
        print(f"\nAI: {answer}")

Example Run

Suppose you have a PDF of your company’s HR policies. Running the script:

python talk_to_doc.py

You can now ask:

python talk_to_doc.py
Chat with your document! (type 'exit' to quit)

Your question: What is this document about?
AI: The document discusses the development and features of an open-source document conversion tool called Docling, which focuses on ensuring that documents are free to use. It highlights the sources of data used for the tool, the challenges of annotating scanned documents, and the preparation work involved in using a cloud-native platform for visual annotation. Additionally, it mentions the gap in the market for open-source tools compared to commercial software for document understanding and conversion, emphasizing the capabilities of Docling in layout analysis and table structure recognition.

Conclusion

The “talk to your document” principle is about making information conversational and accessible. With just a few open-source libraries and a language model, you can transform static files into interactive knowledge companions.