Coursify

Retrieval-Augmented Generation (RAG) — From Fundamentals to Production-Ready Agentic RAG Systems

Hands-on Document Processing Pipeline

35 mins

Build a high-fidelity ingestion script using LangChain. This section provides a production-grade blueprint for loading, cleaning, chunking, and enriching technical data.

Learning Goals

  • Assemble a multi-stage ingestion pipeline in Python.
  • Implement custom separators for higher-precision chunking.
  • Validate the quality of processed chunks using automated checks.

The Production Blueprint

In this section, we move from theory to implementation. We will build a pipeline that transforms a raw, messy technical PDF into high-fidelity context for an AI.

Our pipeline will use the Recursive Character Splitter with an Overlap Buffer to ensure that no critical information is lost at the boundaries of our chunks.

1from langchain_community.document_loaders import PyPDFLoader
2from langchain_text_splitters import RecursiveCharacterTextSplitter
3
4# 1. High-Fidelity Loading
5loader = PyPDFLoader("manuals/vlan_configuration_v2.pdf")
6raw_docs = loader.load()
7
8# 2. Strategic Chunking
9# 800 chars is the 'sweet spot' for technical procedures
10splitter = RecursiveCharacterTextSplitter(
11    chunk_size=800,
12    chunk_overlap=120,
13    separators=["\n\n", "\n", ".", " ", ""],
14    add_start_index=True
15)
16
17# 3. Execution
18processed_chunks = splitter.split_documents(raw_docs)
19
20# 4. Metadata Enrichment Loop
21for i, chunk in enumerate(processed_chunks):
22    chunk.metadata["chunk_id"] = f"vlan_man_{i}"
23    chunk.metadata["ingestion_date"] = "2026-05-10"
24    chunk.metadata["priority"] = "high"

Building Production Ingestion Pipelines

Building Your Ingestion Script

  1. 1
    Step 1

    Identify the document type. For technical data, use PyPDFLoader. For web documentation, use WebBaseLoader or FirecrawlLoader.

  2. 2
    Step 2

    Set your chunk_size based on the complexity of your data. Large chunk sizes (1500+) are better for legal text; smaller sizes (400-600) are better for Q&A.

  3. 3
    Step 3

    Define a 15% chunk_overlap. This ensures that even if a sentence is split, the model sees enough of the 'neighboring' text to maintain context.

  4. 4
    Step 4

    Run a simple loop to print the first 3 chunks and their metadata. Check for 'broken' words at the start or end of chunks.

Knowledge Check

Question 1 of 3
Q1Single choice

In the code above, what is the benefit of setting 'add_start_index=True'?

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