LangChain chunking

LangChain chunking is the process of slicing long documents into smaller, overlapping pieces so a large language model (LLM) can embed, retrieve, and reason over them efficiently. In the LangChain framework you create a Textsplitter, set parameters such as chunk_size (token or character count) and chunk_overlap (to preserve context), then pass raw text, PDFs, or HTML through it. Each chunk is independently vector-embedded and stored in a database such as Chroma or Pinecone. During a Retrieval-Augmented Generation (RAG) query, LangChain computes an embedding for the user prompt, performs similarity search against the stored chunks, and feeds only the top-k snippets to the LLM—dramatically cutting token cost and hallucination risk. Tuning chunk size optimizes the trade-off between semantic completeness and recall; typical values range from 256 to 1 024 tokens with 10–20 % overlap. Because chunking runs offline at ingestion time, you can re-index quickly when docs change, enabling near-real-time knowledge updates for chatbots, copilots, and analytics agents.