Chunking and Embedding Documents
Prepare a searchable knowledge base.
Chunking and Embedding Documents is a free NLP Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Documents Are Too Big to Search Whole
A long PDF holds many topics at once. To retrieve precisely, you first split it into smaller pieces called chunks.
What Makes a Good Chunk
A good chunk is one coherent thought: a paragraph or two. Too big buries the answer; too small loses the surrounding context.
Splitting by Size
The simplest method cuts text every fixed number of characters or tokens. It is quick but can slice a sentence in half.
chunks = [text[i:i+500] for i in range(0, len(text), 500)]Overlapping Chunks
To avoid cutting an idea apart, let chunks share an overlap. Repeating the last lines keeps context flowing across boundaries.
Splitting on Structure
Smarter splitters break on paragraphs or headings first. Respecting structure keeps each chunk on a single, clean topic.
From Text to Vectors
Search needs numbers, not words. An embedding turns each chunk into a dense vector that captures its meaning.
Meaning Lives in Distance
Embeddings place similar text close together. Two chunks about the same idea sit near each other in vector space.
Calling an Embedding Model
You pass text to an embedding model and get back a list of floats. The same model must encode both chunks and queries.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")Encoding Your Chunks
Run every chunk through the model to build its vector. One call can encode the whole list at once for speed.
vectors = model.encode(chunks)Vector Dimensions
Each vector has a fixed length, its dimension. A small model may give 384 numbers; larger ones give 768 or more.
print(vectors.shape) # (n_chunks, 384)Store Chunk and Vector Together
Keep each vector linked to its original text and source. Later you retrieve by vector but show the human-readable chunk.
Quick Check
Consider why we add overlap when splitting documents.
Recap
You split documents into chunks, optionally overlapping, then embed each into a vector. Store text and vector together for retrieval. ✅
Frequently asked questions
Is the “Chunking and Embedding Documents” lesson free?
Yes — the full text of “Chunking and Embedding Documents” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Chunking and Embedding Documents”?
Prepare a searchable knowledge base. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Chunking and Embedding Documents” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Why LLMs Need Retrieval
- Chunking and Embedding Documents
- Vector Search With a Vector Store
- Wiring Retrieval Into the Prompt