Example where LangChain uses Transformer Library to summarize PDF documents

Here's an example where LangChain uses the Hugging Face Transformers library to summarize PDF documents locally.

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🔑 What Will This Example Do?

1. Load a PDF document.
2. Split the text into smaller chunks.
3. Use Hugging Face Transformers to generate summaries for each chunk.
4. Combine the summaries into a final summary.

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Prerequisites

Install the necessary libraries:

pip install langchain transformers pypdf

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Code Example

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import HuggingFacePipeline
from langchain.chains.summarize import load_summarize_chain
from transformers import pipeline

# 1. Load the PDF Document
pdf_path = "document.pdf"  # Replace with your PDF file path
loader = PyPDFLoader(pdf_path)
documents = loader.load()

# 2. Split Text into Chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = text_splitter.split_documents(documents)

# 3. Initialize Hugging Face Summarization Pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Wrap the pipeline with LangChain
llm = HuggingFacePipeline(pipeline=summarizer)

# 4. Create Summarization Chain
summarize_chain = load_summarize_chain(llm, chain_type="map_reduce")

# 5. Generate the Summary
summary = summarize_chain.run(chunks)

print("Summary:")
print(summary)

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🔑 How It Works:

1. The PDF is loaded and converted into text.
2. The text is split into manageable chunks (1000 characters with overlap).
3. Each chunk is summarized independently using the BART model from Hugging Face.
4. The smaller summaries are combined into a final summary using LangChain's map_reduce summarization chain.

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📌 Optional Improvements:

• Use larger models like google/flan-t5-large for better summaries.
• Add streaming summarization for faster feedback.
• Use local embeddings to filter out irrelevant chunks before summarization.

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When to Use This Setup?

Use Case	Recommendation
Local Summarization	✅ Best for Privacy
Long Documents	✅ Chunk-based Summarization
Custom Models	🔥 Hugging Face Integration

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