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In this lesson, we’ll demonstrate how to load a PDF file (handbook.pdf) using LangChain’s PyPDFLoader, split it into pages, and implement a recursive chunking strategy for semantic search preprocessing.

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1. Loading the PDF

Example output:

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2. Configuring Recursive Character Splitter

To enable effective semantic search, we’ll split each page into smaller chunks with overlap to preserve context across boundaries.
Adjust chunk_size and chunk_overlap based on document length, LLM context window, and vector-database performance.

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3. Why Overlap Matters

  • Context preservation: Prevents sentences from cutting off abruptly.
  • Improved retrieval: Ensures related queries find relevant segments.

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4. Inspecting the Chunks

Check how many chunks were generated and preview the first two:
Example output:
Each Document object contains:
  • page_content: Up to 200 characters of text.
  • metadata: Source file path and original page number, useful for citation and retrieval.

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Next Steps

With chunking complete, you can:
  1. Generate embeddings for each chunk.
  2. Build a vector index for semantic retrieval.
  3. Integrate into a Retrieval-Augmented Generation (RAG) pipeline.

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