LangChain Text Splitters & Chunking
Description
Why can’t you just upload a 100-page PDF to an LLM? In this video, we explore the critical step of Document Splitting and Chunking—the secret to building high-performance RAG systems that never hit token limits.
Even if your LLM has a large context window, breaking data into smaller, semantically meaningful "chunks" is essential for accurate retrieval and cost-efficiency. We explain exactly how these splitters work and which strategies you should use for different types of data.
What we cover in this lesson:
The Goal of Chunking: Fitting data into the LLM context window and improving search relevance.
How Text Splitters Work: The process of splitting, combining, and creating overlaps for context retention.
Splitting Strategies: Choosing between character counts, token counts, and semantic/sectional chunking.
LangChain’s Built-in Splitters:
Recursive Character Text Splitter: Why this is the "gold standard" for most use cases.
Character Text Splitter: A simple, specialized approach.
Token-based Splitting: Using TikToken (OpenAI), spaCy, and Sentence Transformers.
Unstructured.io: Advanced chunking based on document titles and headers.
Mastering chunking is the difference between an AI that gives generic answers and one that finds the exact needle in the haystack.
#RAG #LangChain #TextChunking #GenerativeAI #NLP #Python #OpenAI #VectorSearch #MachineLearning #AIEngineering
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